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Deep learning offers promise in enhancing low-quality images by addressing weak fluorescence signals, especially in deep in vivo mouse brain imaging. However, current methods struggle with photon scarcity and noise within in vivo deep mouse brains, and often neglecting tissue preservation. In this study, we propose an innovative in vivo cortical fluorescence image restoration approach, combining signal enhancement, denoising, and inpainting. We curated a deep brain cortical image dataset and developed a novel deep brain coordinate attention restoration network (DeepCAR), integrating coordinate attention with optimized residual networks. Our method swiftly and accurately restores deep cortex images exceeding 800 μm, preserving small-scale tissue structures. It boosts the peak signal-to-noise ratio (PSNR) by 6.94 dB for weak signals and 11.22 dB for large noisy images. Crucially, we validate the effectiveness on external datasets with diverse noise distributions, structural features compared to those in our training data, showcasing real-time high-performance image restoration capabilities., (© 2024 Wiley‐VCH GmbH.)

Background: Crop diseases can lead to significant yield losses and food shortages if not promptly identified and managed by farmers. With the advancements in convolutional neural networks (CNN) and the widespread availability of smartphones, automated and accurate identification of crop diseases has become feasible. However, although previous studies have achieved high accuracy (>95%) under laboratory conditions (Lab) using mixed data sets of multiple crops, these models often falter when deployed under field conditions (Field). In this study, we aimed to evaluate disease identification accuracy under Lab, Field, and Mixed (Lab and Field) conditions using an assembled data set encompassing 14 diseases of apple (Malus × domestica Borkh.), potato (Solanum tuberosum L.), and tomato (Solanum lycopersicum L.). In addition, we investigated the impact of model architectures, parameter sizes, and crop-specific models (CSMs) on accuracy, using DenseNets, ResNets, MobileNetV3, EfficientNet, and VGG Nets., Results: Our results revealed a decrease in accuracy across all models from Lab (98.22%) to Mixed (91.76%) to Field (71.55%) conditions. Interestingly, disease classification accuracy showed minimal variation across model architectures and parameter sizes: Lab (97.61-98.76%), Mixed (90.76-92.31%), and Field (68.56-73.81%). Although CSMs were found to reduce inter-crop disease misclassifications, they also led to a slight increase in intra-crop misclassifications., Conclusion: Our findings underscore the importance of enriching data representation and volumes over employing new model architectures. Furthermore, the need for more field-specific images was highlighted. Ultimately, these insights contribute to the advancement of crop disease identification applications, facilitating their practical implementation in farmer's fields. © 2024 Society of Chemical Industry., (© 2024 Society of Chemical Industry.)

Non-invasive screening for bladder cancer is crucial for treatment and postoperative follow-up. This study combines digital microfluidics (DMF) technology with fluorescence lifetime imaging microscopy (FLIM) for urine analysis and introduces a novel non-invasive bladder cancer screening technique. Initially, the DMF was utilized to perform preliminary screening and enrichment of urine exfoliated cells from 54 participants, followed by cell staining and FLIM analysis to assess the viscosity of the intracellular microenvironment. Subsequently, a deep learning residual convolutional neural network was employed to automatically classify FLIM images, achieving a three-class prediction of high-risk (malignant), low-risk (benign), and minimal risk (normal) categories. The results demonstrated a high consistency with pathological diagnosis, with an accuracy of 91% and a precision of 93%. Notably, the method is sensitive for both high-grade and low-grade bladder cancer cases. This highly accurate non-invasive screening method presents a promising approach for bladder cancer screening with significant clinical application potential., (© 2024 Wiley‐VCH GmbH.)

Purpose: To explore the value of deep learning-based multi-parametric magnetic resonance imaging (mp-MRI) nomogram in predicting the Ki-67 expression in rectal cancer., Methods: The data of 491 patients with rectal cancer from two centers were retrospectively analyzed and divided into training, internal validation, and external validation sets. They were categorized into high- and low-expression group based on postoperative pathological Ki-67 expression. Each patient's mp-MRI data were analyzed to extract and select the most relevant features of deep learning, and a deep learning model was constructed. Independent predictive risk factors were identified and incorporated into a clinical model, and the clinical and deep learning models were combined to obtain a nomogram for the prediction of Ki-67 expression. The performance characteristics of the DL-model, clinical model, and nomogram were assessed using ROCs, calibration curve, decision curve, and clinical impact curve analysis., Results: The strongest deep learning features were extracted and screened from mp-MRI data. Two independent predictive factors, namely Magnetic Resonance Imaging T (mrT) staging and differentiation degree, were identified through clinical feature selection. Three models were constructed: a deep learning (DL)-model, a clinical model, and a nomogram. The AUCs of clinical model in the training, internal validation, and external validation set were 0.69, 0.78, and 0.67, respectively. The AUCs of the deep model and nomogram ranged from 0.88 to 0.98. The prediction performance of the deep learning model and nomogram was significantly better than the clinical model (P < 0.001)., Conclusion: The nomogram based on deep learning can help clinicians accurately and conveniently predict the expression status of Ki-67 in rectal cancer., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

In the world, grapes are considered as the most significant fruit, and it comprises various nutrients, like Vitamin C and it is utilized to produce wines and raisins. The major six general grape leaf diseases and pests are brown spots, leaf blight, downy mildew, anthracnose, and black rot. However, the existing manual detection methods are time-consuming and require more efforts. In this paper, an effectual grape leaf disease finding and percentage of pesticide detection approach is devised usingan optimized deep learning scheme. Here, the input image is pre-processed and then, black spot segmentation is done using proposed Taylor Remora Optimization Procedure (TROA). The TROA is the combination of Taylor concept and Remora Optimization Algorithm (ROA). After that, the multi-classification of grape leaf disease is performed to classify the disease as Black rot, Black measles, Isariopsis leaf spot and healthy. Accordingly, the training process of the Deep Neuro-Fuzzy Optimizer (DNFN) is done using Sine Cosine Butterfly Optimization (SCBO). Then, pesticide classification is done using Deep Maxout Network (DMN) and the training of the DMN is done using the Monarch Anti Corona Optimization (MACO) algorithm. Finally, the pesticide percentage level detection is performed using Deep Belief Network (DBN), which is trained by the TROA. The devised scheme obtained highest accuracy of 0.9327, sensitivity of 0.9383, and 0.9429. Thus, this method can assist as an effectual decision provision system for assisting the farmers to find the percentage of pesticide affected in grape leaf diseases., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Objectives: Anti-HER2 targeted therapy significantly reduces risk of relapse in HER2 + breast cancer. New measures are needed for a precise risk stratification to guide (de-)escalation of anti-HER2 strategy., Methods: A total of 726 HER2 + cases who received no/single/dual anti-HER2 targeted therapies were split into three respective cohorts. A deep learning model (DeepTEPP) based on preoperative breast magnetic resonance (MR) was developed. Patients were scored and categorized into low-, moderate-, and high-risk groups. Recurrence-free survival (RFS) was compared in patients with different risk groups according to the anti-HER2 treatment they received, to validate the value of DeepTEPP in predicting treatment efficacy and guiding anti-HER2 strategy., Results: DeepTEPP was capable of risk stratification and guiding anti-HER2 treatment strategy: DeepTEPP-Low patients (60.5%) did not derive significant RFS benefit from trastuzumab (p = 0.144), proposing an anti-HER2 de-escalation. DeepTEPP-Moderate patients (19.8%) significantly benefited from trastuzumab (p = 0.048), but did not obtain additional improvements from pertuzumab (p = 0.125). DeepTEPP-High patients (19.7%) significantly benefited from dual HER2 blockade (p = 0.045), suggesting an anti-HER2 escalation., Conclusions: DeepTEPP represents a pioneering MR-based deep learning model that enables the non-invasive prediction of adjuvant anti-HER2 effectiveness, thereby providing valuable guidance for anti-HER2 (de-)escalation strategies. DeepTEPP provides an important reference for choosing the appropriate individualized treatment in HER2 + breast cancer patients, warranting prospective validation., Clinical Relevance Statement: We built an MR-based deep learning model DeepTEPP, which enables the non-invasive prediction of adjuvant anti-HER2 effectiveness, thus guiding anti-HER2 (de-)escalation strategies in early HER2-positive breast cancer patients., Key Points: • DeepTEPP is able to predict anti-HER2 effectiveness and to guide treatment (de-)escalation. • DeepTEPP demonstrated an impressive prognostic efficacy for recurrence-free survival and overall survival. • To our knowledge, this is one of the very few, also the largest study to test the efficacy of a deep learning model extracted from breast MR images on HER2-positive breast cancer survival and anti-HER2 therapy effectiveness prediction., (© 2024. The Author(s).)

Short-length antimicrobial peptides (AMPs) have been demonstrated to have intensified antimicrobial activities against a wide spectrum of microbes. Therefore, exploration of novel and promising short AMPs is highly essential in developing various types of antimicrobial drugs or treatments. In addition to experimental approaches, computational methods have been developed to improve screening efficiency. Although existing computational methods have achieved satisfactory performance, there is still much room for model improvement. In this study, we proposed iAMP-DL, an efficient hybrid deep learning architecture, for predicting short AMPs. The model was constructed using two well-known deep learning architectures: the long short-term memory architecture and convolutional neural networks. To fairly assess the performance of the model, we compared our model with existing state-of-the-art methods using the same independent test set. Our comparative analysis shows that iAMP-DL outperformed other methods. Furthermore, to assess the robustness and stability of our model, the experiments were repeated 10 times to observe the variation in prediction efficiency. The results demonstrate that iAMP-DL is an effective, robust, and stable framework for detecting promising short AMPs. Another comparative study of different negative data sampling methods also confirms the effectiveness of our method and demonstrates that it can also be used to develop a robust model for predicting AMPs in general. The proposed framework was also deployed as an online web server with a user-friendly interface to support the research community in identifying short AMPs., (© 2024 The Authors. Proteomics published by Wiley‐VCH GmbH.)

Meiotic crossovers play a vital role in proper chromosome segregation and evolution of most sexually reproducing organisms. Meiotic recombination can be visually observed in Saccharomyces cerevisiae tetrads using linked spore-autonomous fluorescent markers placed at defined intervals within the genome, which allows for analysis of meiotic segregation without the need for tetrad dissection. To automate the analysis, we developed a deep learning-based image recognition and classification pipeline for high-throughput tetrad detection and meiotic crossover classification. As a proof of concept, we analyzed a large image data set from wild-type and selected gene knock-out mutants to quantify crossover frequency, interference, chromosome missegregation, and gene conversion events. The deep learning-based method has the potential to accelerate the discovery of new genes involved in meiotic recombination in S. cerevisiae such as the underlying factors controlling crossover frequency and interference., (© 2024 The Author(s). Yeast published by John Wiley & Sons Ltd.)

Purpose: Radiotherapy planning incorporating functional lung images has the potential to reduce pulmonary toxicity. Free-breathing 4DCT-derived ventilation image (CTVI) may help quantify lung function. This study introduces a novel deep-learning model directly translating planning CT images into CTVI. We investigated the accuracy of generated images and the impact on functional avoidance planning., Materials and Methods: Paired planning CT and 4DCT scans from 48 patients with NSCLC were randomized to training (n = 41) and testing (n = 7) data sets. The ventilation maps were generated from 4DCT using a Jacobian-based algorithm to provide ground truth labels (CTVI 4DCT ). A 3D U-Net-based model was trained to map CT to synthetic CTVI (CTVI Syn ) and validated using fivefold cross-validation. The highest-performing model was applied to the testing set. Spearman's correlation (r s ) and Dice similarity coefficient (DSC) determined voxel-wise and functional-wise concordance between CTVI 4DCT and CTVI Syn . Three plans were designed per patient in the testing set: one clinical plan without CTVI and two functional avoidance plans combined with CTVI 4DCT or CTVI Syn , aimed at sparing high-functional lungs defined as the top 50% of the percentile ventilation ranges. Dose-volume (DVH) parameters regarding the planning target volume (PTV) and organs at risk (OARs) were recorded. Radiation pneumonitis (RP) risk was estimated using a dose-function (DFH)-based normal tissue complication probability (NTCP) model., Results: CTVI Syn showed a mean rs value of 0.65 ± 0.04 compared to CTVI 4DCT . Mean DSC values over the top 50% and 60% of ventilation ranges were 0.41 ± 0.07 and 0.52 ± 0.10, respectively. In the test set (n = 7), all patients' RP-risk benefited from CTVI 4DCT -guided plans (Risk mean&#95;4DCT&#95;vs&#95;Clinical : 29.24% vs. 49.12%, P = 0.016), and six patients benefited from CTVI Syn -guided plans (Risk mean&#95;Syn&#95;vs&#95;Clinical : 31.13% vs. 49.12%, P = 0.022). There were no significant differences in DVH and DFH metrics between CTVI Syn and CTVI 4DCT -guided plan (P > 0.05)., Conclusion: Using deep-learning techniques, CTVI Syn generated from planning CT exhibited a moderate-to-high correlation with CTVI 4DCT . The CTVI Syn -guided plans were comparable to the CTVI 4DCT -guided plans, effectively reducing pulmonary toxicity in patients while maintaining acceptable plan quality. Further prospective trials are needed to validate these findings., (© 2024. The Author(s) under exclusive licence to Japan Radiological Society.)

Objectives: The primary objective of this research is to improve the average classification performance for specific movements in patients with cervical spinal cord injury (SCI)., Methods: The study utilizes a low-frequency multi-class electroencephalography (EEG) dataset from Graz University of Technology. The research combines convolutional neural network (CNN) and long-short-term memory (LSTM) architectures to uncover neural correlations between temporal and spatial aspects of the EEG signals associated with attempted arm and hand movements. To achieve this, three different methods are used to select relevant features, and the proposed model's robustness against variations in the data is validated using 10-fold cross-validation (CV). The research also investigates subject-specific adaptation in an online paradigm, extending movement classification proof-of-concept., Results: The combined CNN-LSTM model, enhanced by three feature selection methods, demonstrates robustness with a mean accuracy of 75.75 % and low standard deviation (+/- 0.74 %) in 10-fold cross-validation, confirming its reliability., Conclusions: In summary, this research aims to make valuable contributions to the field of neuro-technology by developing EEG-controlled assistive devices using a generalized brain-computer interface (BCI) and deep learning (DL) framework. The focus is on capturing high-level spatiotemporal features and latent dependencies to enhance the performance and usability of EEG-based assistive technologies., (© 2024 Walter de Gruyter GmbH, Berlin/Boston.)

Rapid and precise estimation of glycosylated serum protein (GSP) of human serum is of great importance for the treatment and diagnosis of diabetes mellitus. In this study, we propose a novel method for estimation of GSP level based on the combination of deep learning and time domain nuclear magnetic resonance (TD-NMR) transverse relaxation signal of human serum. Specifically, a principal component analysis (PCA)-enhanced one-dimensional convolutional neural network (1D-CNN) is proposed to analyze the TD-NMR transverse relaxation signal of human serum. The proposed algorithm is proved by accurate estimation of GSP level for the collected serum samples. Furthermore, the proposed algorithm is compared with 1D-CNN without PCA, long short-term memory network (LSTM) and some conventional machine learning algorithms. The results indicate that PCA-enhanced 1D-CNN (PC-1D-CNN) has the minimum error. This study proves that proposed method is feasible and superior to estimate GSP level of human serum using TD-NMR transverse relaxation signals., (© 2023. The Author(s), under exclusive licence to The Japan Society for Analytical Chemistry.)

In the context of environmental and social applications, the analysis of land use and land cover (LULC) holds immense significance. The growing accessibility of remote sensing (RS) data has led to the development of LULC benchmark datasets, especially pivotal for intricate image classification tasks. This study addresses the scarcity of such benchmark datasets across diverse settings, with a particular focus on the distinctive landscape of India. The study entails the creation of patch-based datasets, consisting of 4000 labelled images spanning four distinct LULC classes derived from Sentinel-2 satellite imagery. For the subsequent classification task, three traditional machine learning (ML) models and three convolutional neural networks (CNNs) were employed. Despite facing several challenges throughout the process of dataset generation and subsequent classification, the CNN models consistently attained an overall accuracy of 90% or more. Notably, one of the ML models stood out with 96% accuracy, surpassing CNNs in this specific context. The study also conducts a comparative analysis of ML models on existing benchmark datasets, revealing higher prediction accuracy when dealing with fewer LULC classes. Thus, the selection of an appropriate model hinges on the given task, available resources, and the necessary trade-offs between performance and efficiency, particularly crucial in resource-constrained settings. The standardized benchmark dataset contributes valuable insights into the relative performance of deep CNN and ML models in LULC classification, providing a comprehensive understanding of their strengths and weaknesses., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Purpose: Progression of hip osteoarthritis (hip OA) leads to pain and disability, likely leading to surgical treatment such as hip arthroplasty at the terminal stage. The severity of hip OA is often classified using the Crowe and Kellgren-Lawrence (KL) classifications. However, as the classification is subjective, we aimed to develop an automated approach to classify the disease severity based on the two grades using digitally-reconstructed radiographs from CT images., Methods: Automatic grading of the hip OA severity was performed using deep learning-based models. The models were trained to predict the disease grade using two grading schemes, i.e., predicting the Crowe and KL grades separately, and predicting a new ordinal label combining both grades and representing the disease progression of hip OA. The models were trained in classification and regression settings. In addition, the model uncertainty was estimated and validated as a predictor of classification accuracy. The models were trained and validated on a database of 197 hip OA patients, and externally validated on 52 patients. The model accuracy was evaluated using exact class accuracy (ECA), one-neighbor class accuracy (ONCA), and balanced accuracy., Results: The deep learning models produced a comparable accuracy of approximately 0.65 (ECA) and 0.95 (ONCA) in the classification and regression settings. The model uncertainty was significantly larger in cases with large classification errors ( P < 6 e - 3 )., Conclusions: In this study, an automatic approach for grading hip OA severity from CT images was developed. The models have shown comparable performance with high ONCA, which facilitates automated grading in large-scale CT databases and indicates the potential for further disease progression analysis. Classification accuracy was correlated with the model uncertainty, which would allow for the prediction of classification errors. The code will be made publicly available at https://github.com/NAIST-ICB/HipOA-Grading ., (© 2024. CARS.)

This study executes a quantitative and visual investigation on the effectiveness of data augmentation and hyperparameter optimization on the accuracy of deep learning-based segmentation of LGG tumors. The study employed the MobileNetV2 and ResNet backbones with atrous convolution in DeepLabV3+ structure. The Grad-CAM tool was also used to interpret the effect of augmentation and network optimization on segmentation performance. A wide investigation was performed to optimize the network hyperparameters. In addition, the study examined 35 different models to evaluate different data augmentation techniques. The results of the study indicated that incorporating data augmentation techniques and optimization can improve the performance of segmenting brain LGG tumors up to 10%. Our extensive investigation of the data augmentation techniques indicated that enlargement of data from 90° and 225° rotated data,up to down and left to right flipping are the most effective techniques. MobilenetV2 as the backbone,"Focal Loss" as the loss function and "Adam" as the optimizer showed the superior results. The optimal model (DLG-Net) achieved an overall accuracy of 96.1% with a loss value of 0.006. Specifically, the segmentation performance for Whole Tumor (WT), Tumor Core (TC), and Enhanced Tumor (ET) reached a Dice Similarity Coefficient (DSC) of 89.4%, 70.1%, and 49.9%, respectively. Simultaneous visual and quantitative assessment of data augmentation and network optimization can lead to an optimal model with a reasonable performance in segmenting the LGG tumors., (© 2024. The Author(s) under exclusive licence to Biomedical Engineering Society.)

Background: Deep learning has shown promising results to generate MRI-based synthetic CTs and to enable accurate proton dose calculations on MRIs. For clinical implementation of synthetic CTs, quality assurance tools that verify their quality and reliability are required but still lacking., Purpose: This study aims to evaluate the predictive value of uncertainty maps generated with Monte Carlo dropout (MCD) for verifying proton dose calculations on deep-learning-based synthetic CTs (sCTs) derived from MRIs in online adaptive proton therapy., Methods: Two deep-learning models (DCNN and cycleGAN) were trained for CT image synthesis using 101 paired CT-MR images. sCT images were generated using MCD for each model by performing 10 inferences with activated dropout layers. The final sCT was obtained by averaging the inferred sCTs, while the uncertainty map was obtained from the HU variance corresponding to each voxel of 10 sCTs. The resulting uncertainty maps were compared to the observed HU-, range-, WET-, and dose-error maps between the sCT and planning CT. For range and WET errors, the generated uncertainty maps were projected along the 90-degree angle. To evaluate the dose distribution, a mask based on the 5%-isodose curve was applied to only include voxels along the beam paths. Pearson's correlation coefficients were calculated to determine the correlation between the uncertainty maps and HUs, range, WET, and dose errors. To evaluate the dosimetric accuracy of synthetic CTs, clinical proton treatment plans were recalculated and compared to the pCTs RESULTS: Evaluation of the correlation showed an average of r = 0.92 ± 0.03 and r = 0.92 ± 0.03 for errors between uncertainty-HU, r = 0.66 ± 0.09 and r = 0.62 ± 0.06 between uncertainty-range, r = 0.64 ± 0.06 and r = 0.58 ± 0.07 between uncertainty-WET, and r = 0.65 ± 0.09 and r = 0.67 ± 0.07 between uncertainty and dose difference for DCNN and cycleGAN model, respectively. Dosimetric comparison for target volumes showed an average 3%/3 mm gamma pass rate of 99.76 ± 0.43 (DCNN) and 99.10 ± 1.27 (cycleGAN)., Conclusion: The observed correlations between uncertainty maps and the various metrics (HU, range, WET, and dose errors) demonstrated the potential of MCD-based uncertainty maps as a reliable QA tool to evaluate the accuracy of deep learning-based sCTs., (© 2023 The Authors. Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Machine learning (ML) techniques have shown great potential in cardiovascular surgery, including real-time stenosis recognition, detection of stented coronary anomalies, and prediction of in-stent restenosis (ISR). However, estimating neointima evolution poses challenges for ML models due to limitations in manual measurements, variations in image quality, low data availability, and the difficulty of acquiring biological quantities. An effective in silico model is necessary to accurately capture the mechanisms leading to neointimal hyperplasia. Physics-informed neural networks (PINNs), a novel deep learning (DL) method, have emerged as a promising approach that integrates physical laws and measurements into modeling. PINNs have demonstrated success in solving partial differential equations (PDEs) and have been applied in various biological systems. This paper aims to develop a robust multiphysics surrogate model for ISR estimation using the physics-informed DL approach, incorporating biological constraints and drug elution effects. The model seeks to enhance prediction accuracy, provide insights into disease progression factors, and promote ISR diagnosis and treatment planning. A set of coupled advection-reaction-diffusion type PDEs is constructed to track the evolution of the influential factors associated with ISR, such as platelet-derived growth factor (PDGF), the transforming growth factor- β (TGF- β ), the extracellular matrix (ECM), the density of smooth muscle cells (SMC), and the drug concentration. The nature of PINNs allows for the integration of patient-specific data (procedure-related, clinical and genetic, etc.) into the model, improving prediction accuracy and assisting in the optimization of stent implantation parameters to mitigate risks. This research addresses the existing gap in predictive models for ISR using DL and holds the potential to enhance patient outcomes through predictive risk assessment., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The main goal of this research is the interpretability of deep learning (DL) model output (e.g., CNN and LSTM) used to map land susceptibility to subsidence hazard by means of different techniques. For this purpose, an inventory map of land subsidence (LS) is prepared based on fieldwork and a record of LS presence points, and 16 features controlling LS were mapped. Thereafter, 11 effective features controlling LS were identified by means of a particle swarm optimization (PSO) algorithm, which was then used as input in the CNN and LSTM predictive models. To address the inherent black box nature of DL models, six interpretation methods (feature interaction, permutation importance plot (PFIM), bar plot, SHapley Additive exPlanations (SHAP) main plot, heatmap plot, and waterfall plot) were used to interpret the predictive model outputs. Both models (CNN and LSTM) had AUC > 90 and therefore provided excellent accuracy for mapping LS hazard. According to the most accurate model-the CNN predictive model-the range from very low to very high hazard classes occupied 20%, 20%, 25%, 16.3%, and 18.7% of the study area, respectively. According to three plots (bar plot, SHAP main plot, and heatmap plot), which were constructed based on the SHAP value, distance from the well, GDR and DEM were identified as the three most important features with the highest impact on the DL model output. The results of the waterfall plot indicate two effective features consisting of distance from the well and coarse fragment, and two effective features comprising landuse and DEM, contributed negatively and positively to LS, respectively. Overall, these explanation techniques can address critical concerns with respect to the interpretability of sophisticated DL predictive models., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Tea is an important cash crop that is often consumed by chewing pests, resulting in reduced yields and economic losses. It is important to establish a method to quickly identify the degree of damage to tea plants caused by leaf-eating insects and screen green control compounds. This study was performed through the combination of deep learning and targeted metabolomics, in vitro feeding experiment, enzymic analysis and transient genetic transformation. A small target damage detection model based on YOLOv5 with Transformer Prediction Head (TPH-YOLOv5) algorithm for the tea canopy level was established. Orthogonal partial least squares (OPLS) was used to analyze the correlation between the degree of damage and the phenolic metabolites. A potential defensive compound, (-)-epicatechin-3-O-caffeoate (EC-CA), was screened. In vitro feeding experiments showed that compared with EC and epicatechin gallate, Ectropis grisescens exhibited more significant antifeeding against EC-CA. In vitro enzymatic experiments showed that the hydroxycinnamoyl transferase (CsHCTs) recombinant protein has substrate promiscuity and can catalyze the synthesis of EC-CA. Transient overexpression of CsHCTs in tea leaves effectively reduced the degree of damage to tea leaves. This study provides important reference values and application prospects for the effective monitoring of pests in tea gardens and screening of green chemical control substances., (© 2023 John Wiley & Sons Ltd.)

Quantitative structure-activity relationship (QSAR) modelling, an approach that was introduced 60 years ago, is widely used in computer-aided drug design. In recent years, progress in artificial intelligence techniques, such as deep learning, the rapid growth of databases of molecules for virtual screening and dramatic improvements in computational power have supported the emergence of a new field of QSAR applications that we term 'deep QSAR'. Marking a decade from the pioneering applications of deep QSAR to tasks involved in small-molecule drug discovery, we herein describe key advances in the field, including deep generative and reinforcement learning approaches in molecular design, deep learning models for synthetic planning and the application of deep QSAR models in structure-based virtual screening. We also reflect on the emergence of quantum computing, which promises to further accelerate deep QSAR applications and the need for open-source and democratized resources to support computer-aided drug design., (© 2023. Springer Nature Limited.)

Biodiversity is being lost at an unprecedented rate on Earth. As a first step to more effectively combat this process we need efficient methods to monitor biodiversity changes. Recent technological advance can provide powerful tools (e.g. camera traps, digital acoustic recorders, satellite imagery, social media records) that can speed up the collection of biological data. Nevertheless, the processing steps of the raw data served by these tools are still painstakingly slow. A new computer technology, deep learning based artificial intelligence, might, however, help. In this short and subjective review I oversee recent technological advances used in conservation biology, highlight problems of processing their data, shortly describe deep learning technology and show case studies of its use in conservation biology. Some of the limitations of the technology are also highlighted., (© 2024. The Author(s).)

Magnetic resonance imaging (MRI) of the brain has benefited from deep learning (DL) to alleviate the burden on radiologists and MR technologists, and improve throughput. The easy accessibility of DL tools has resulted in a rapid increase of DL models and subsequent peer-reviewed publications. However, the rate of deployment in clinical settings is low. Therefore, this review attempts to bring together the ideas from data collection to deployment in the clinic, building on the guidelines and principles that accreditation agencies have espoused. We introduce the need for and the role of DL to deliver accessible MRI. This is followed by a brief review of DL examples in the context of neuropathologies. Based on these studies and others, we collate the prerequisites to develop and deploy DL models for brain MRI. We then delve into the guiding principles to develop good machine learning practices in the context of neuroimaging, with a focus on explainability. A checklist based on the United States Food and Drug Administration's good machine learning practices is provided as a summary of these guidelines. Finally, we review the current challenges and future opportunities in DL for brain MRI., (© 2023 John Wiley & Sons Ltd.)

Background: We aimed to establish image recognition and survival prediction models using a novel scoring system of cyclin D1 expression pattern in patients with human papillomavirus-negative oral or oropharyngeal squamous cell carcinoma., Methods: The clinicopathological data of 610 patients with human papillomavirus-negative oral/oropharyngeal squamous cell carcinoma were analyzed retrospectively. Cox univariate and multivariate risk regression analyses were performed to compare cyclin D1 expression pattern scoring with the traditional scoring method-cyclin D1 expression level scoring-in relation to patients' overall and progression-free survival. An image recognition model employing the cyclin D1 expression pattern scoring system was established by YOLOv5 algorithms. From this model, two independent survival prediction models were established using the DeepHit and DeepSurv algorithms., Results: Cyclin D1 had three expression patterns in oral and oropharyngeal squamous cell carcinoma cancer nests. Superior to cyclin D1 expression level scoring, cyclin D1 expression pattern scoring was significantly correlated with the prognosis of patients with oral squamous cell carcinoma (p < 0.0001) and oropharyngeal squamous cell carcinoma (p < 0.05). Moreover, it was an independent prognostic risk factor in both oral squamous cell carcinoma (p < 0.0001) and oropharyngeal squamous cell carcinoma (p < 0.05). The cyclin D1 expression pattern-derived image recognition model showed an average test set accuracy of 78.48% ± 4.31%. In the overall survival prediction models, the average concordance indices of the test sets established by DeepSurv and DeepHit were 0.71 ± 0.02 and 0.70 ± 0.01, respectively., Conclusion: Combined with the image recognition model of the cyclin D1 expression pattern, the survival prediction model had a relatively good prediction effect on the overall survival prognosis of patients with human papillomavirus-negative oral or oropharyngeal squamous cell carcinoma., (© 2023 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Objective: This study aimed to evaluate the clinical performance of a deep learning-based motion correction algorithm (MCA) in projection domain for coronary computed tomography angiography (CCTA)., Methods: A total of 192 patients who underwent CCTA examinations were included and divided into 2 groups based on the average heart rate (HR): group 1, 82 patients with HR of <75 beats per minute; group 2, 110 patients with HR of ≥75 beats per minute. The CCTA images were reconstructed with and without MCA. The subjective image quality was graded in terms of vessel visualization, sharpness, diagnostic confidence, and overall image quality using a 5-point scale, where cases with all scores of ≥3 were deemed interpretable. Objective image quality was measured through signal-to-noise ratio and contrast-to-noise ratio in regions relative to the vessels. The image quality scores for 2 reconstructions and effective dose between 2 groups were compared., Results: The mean effective dose was similar between 2 groups. Neither group showed significant difference on objective image quality for 2 reconstructions. Images reconstructed with and without MCA were both found interpretable for group 1, whereas the subjective image quality was significantly improved by the MCA for all 4 metrics in group 2, with the interpretability increased from 80.91% to 99.09%. Compared with group 1, group 2 showed similar interpretability and diagnostic confidence, despite inferior overall image quality., Conclusions: In CCTA examinations, the deep learning-based MCA is capable of improving the image quality and diagnostic confidence for patients with increased HR to a similar level as for those with low HR., Competing Interests: The authors declare no conflict of interest. This work was supported by 1·3·5 project for disciplines of excellence, West China Hospital, Sichuan University (ZYGD18019)., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)

Spartina alterniflora is a halophyte that can survive in high-salinity environments, and it is phylogenetically close to important cereal crops, such as maize and rice. It is of scientific interest to understand why S. alterniflora can live under such extremely stressful conditions. The molecular mechanism underlying its high-saline tolerance is still largely unknown. Here we investigated the possibility that high-affinity K + transporters (HKTs), which function in salt tolerance and maintenance of ion homeostasis in plants, are responsible for salt tolerance in S. alterniflora. To overcome the imprecision and unstable of the gene screening method caused by the conventional sequence alignment, we used a deep learning method, DeepGOPlus, to automatically extract sequence and protein characteristics from our newly assemble S. alterniflora genome to identify SaHKTs. Results showed that a total of 16 HKT genes were identified. The number of S. alterniflora HKTs (SaHKTs) is larger than that in all other investigated plant species except wheat. Phylogenetically related SaHKT members had similar gene structures, conserved protein domains and cis-elements. Expression profiling showed that most SaHKT genes are expressed in specific tissues and are differentially expressed under salt stress. Yeast complementation expression analysis showed that type I members SaHKT1;2, SaHKT1;3 and SaHKT1;8 and type II members SaHKT2;1, SaHKT2;3 and SaHKT2;4 had low-affinity K + uptake ability and that type II members showed stronger K + affinity than rice and Arabidopsis HKTs, as well as most SaHKTs showed preference for Na + transport. We believe the deep learning-based methods are powerful approaches to uncovering new functional genes, and the SaHKT genes identified are important resources for breeding new varieties of salt-tolerant crops., (© 2023 The Authors. The Plant Journal published by Society for Experimental Biology and John Wiley & Sons Ltd.)

Background: Most of superficial soft-tissue masses are benign tumors, and very few are malignant tumors. However, persistent growth, of both benign and malignant tumors, can be painful and even life-threatening. It is necessary to improve the differential diagnosis performance for superficial soft-tissue masses by using deep learning models. This study aimed to propose a new ultrasonic deep learning model (DLM) system for the differential diagnosis of superficial soft-tissue masses., Methods: Between January 2015 and December 2022, data for 1615 patients with superficial soft-tissue masses were retrospectively collected. Two experienced radiologists (radiologists 1 and 2 with 8 and 30 years' experience, respectively) analyzed the ultrasound images of each superficial soft-tissue mass and made a diagnosis of malignant mass or one of the five most common benign masses. After referring to the DLM results, they re-evaluated the diagnoses. The diagnostic performance and concerns of the radiologists were analyzed before and after referring to the results of the DLM results., Results: In the validation cohort, DLM-1 was trained to distinguish between benign and malignant masses, with an AUC of 0.992 (95% CI: 0.980, 1.0) and an ACC of 0.987 (95% CI: 0.968, 1.0). DLM-2 was trained to classify the five most common benign masses (lipomyoma, hemangioma, neurinoma, epidermal cyst, and calcifying epithelioma) with AUCs of 0.986, 0.993, 0.944, 0.973, and 0.903, respectively. In addition, under the condition of the DLM-assisted diagnosis, the radiologists greatly improved their accuracy of differential diagnosis between benign and malignant tumors., Conclusions: The proposed DLM system has high clinical application value in the differential diagnosis of superficial soft-tissue masses., (© 2023. BioMed Central Ltd., part of Springer Nature.)

Motivation: Proteins-protein interactions (PPIs) are important to govern several cellular activities. Amino acid residues, which are located at the interface are known as the binding sites and the information about binding sites helps to understand the binding affinities and functions of protein-protein complexes., Results: We have developed a deep neural network-based method, DeepBSRPred, for predicting the binding sites using protein sequence information and predicted structures from AlphaFold2. Specific sequence and structure-based features include position-specific scoring matrix (PSSM), solvent accessible surface area, conservation score and amino acid properties, and residue depth, respectively. Our method predicted the binding sites with an average F1 score of 0.73 in a dataset of 1236 proteins. Further, we compared the performance with other existing methods in the literature using four benchmark datasets and our method outperformed those methods., Availability and Implementation: The DeepBSRPred web server can be found at https://web.iitm.ac.in/bioinfo2/deepbsrpred/index.html , along with all datasets used in this study. The trained models, the DeepBSRPred standalone source code, and the feature computation pipeline are freely available at https://web.iitm.ac.in/bioinfo2/deepbsrpred/download.html ., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.)

The significance of performing large-depth dynamic microscopic imaging in vivo for life science research cannot be overstated. However, the optical throughput of the microscope limits the available information per unit of time, i.e., it is difficult to obtain both high spatial and temporal resolution at once. Here, a method is proposed to construct a kind of intravital microscopy with high optical throughput, by making near-infrared-II (NIR-II, 900-1880 nm) wide-field fluorescence microscopy learn from two-photon fluorescence microscopy based on a scale-recurrent network. Using this upgraded NIR-II fluorescence microscope, vessels in the opaque brain of a rodent are reconstructed three-dimensionally. Five-fold axial and thirteen-fold lateral resolution improvements are achieved without sacrificing temporal resolution and light utilization. Also, tiny cerebral vessel dilatations in early acute respiratory failure mice are observed, with this high optical throughput NIR-II microscope at an imaging speed of 30 fps., (© 2023 Wiley-VCH GmbH.)

Purpose: Recent development of ultra-low-field (ULF) MRI presents opportunities for low-power, shielding-free, and portable clinical applications at a fraction of the cost. However, its performance remains limited by poor image quality. Here, a computational approach is formulated to advance ULF MR brain imaging through deep learning of large-scale publicly available 3T brain data., Methods: A dual-acquisition 3D superresolution model is developed for ULF brain MRI at 0.055 T. It consists of deep cross-scale feature extraction, attentional fusion of two acquisitions, and reconstruction. Models for T 1 -weighted and T 2 -weighted imaging were trained with 3D ULF image data sets synthesized from the high-resolution 3T brain data from the Human Connectome Project. They were applied to 0.055T brain MRI with two repetitions and isotropic 3-mm acquisition resolution in healthy volunteers, young and old, as well as patients., Results: The proposed approach significantly enhanced image spatial resolution and suppressed noise/artifacts. It yielded high 3D image quality at 0.055 T for the two most common neuroimaging protocols with isotropic 1.5-mm synthetic resolution and total scan time under 20 min. Fine anatomical details were restored with intrasubject reproducibility, intercontrast consistency, and confirmed by 3T MRI., Conclusion: The proposed dual-acquisition 3D superresolution approach advances ULF MRI for quality brain imaging through deep learning of high-field brain data. Such strategy can empower ULF MRI for low-cost brain imaging, especially in point-of-care scenarios or/and in low-income and mid-income countries., (© 2023 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)

Coronary angiography (CAG) is the "gold standard" for diagnosing coronary artery disease (CAD). However, due to the limitation of current imaging methods, the CAG image has low resolution and poor contrast with a lot of artifacts and noise, which makes it difficult for blood vessels segmentation. In this paper, we propose a DBCU-Net for automatic segmentation of CAG images, which is an extension of U-Net, DenseNet with bi-directional ConvLSTM(BConvLSTM). The main contribution of our network is that instead of convolution in the feature extraction of U-Net, we incorporate dense connectivity and the bi-directional ConvLSTM to highlight salient features. We conduct our experiment on our private dataset, and achieve average Accuracy, Precision, Recall and F1-score for coronary artery segmentation of 0.985, 0.913, 0.847 and 0.879 respectively., (© 2023. The Author(s), under exclusive licence to Springer Nature B.V.)

The Manasi region is located in an arid and semi-arid region with fragile ecology and scarce resources. The land use change prediction is important for the management and optimization of land resources. We utilized Sankey diagram, dynamic degree of land use, and landscape indices to explore the temporal and spatial variation of land use and integrated the LSTM and MLP algorithms to predict land use prediction. The MLP-LSTM prediction model retains the spatiotemporal information of land use data to the greatest extent and extracts the spatiotemporal variation characteristics of each grid through a training set. Results showed that (1) from 1990 to 2020, cropland, tree cover, water bodies, and urban areas in the Manasi region increased by 855.3465 km 2 , 271.7136 km 2 , 40.0104 km 2 , and 109.2483 km 2 , respectively, whereas grassland and bare land decreased by 677.7243 km 2 and 598.5945 km 2 , respectively; (2) Kappa coefficients reflect the accuracy of the mode's predictions in terms of quantity. The Kappa coefficients of the land use data predicted by the MLP-LSTM, MLP-ANN, LR, and CA-Markov models were calculated to be 95.58%, 93.36%, 89.48%, and 85.35%, respectively. It can be found that the MLP-LSTM and MLP-ANN models obtain higher accuracy in most levels, while the CA-Markov model has the lowest accuracy. (3) The landscape indices can reflect the spatial configuration characteristics of landscape (land use types), and evaluating the prediction results of land use models using landscape indices can reflect the prediction accuracy of the models in terms of spatial features. The results indicate that the model predicted by MLP-LSTM model conforms to the development trend of land use from 1990 to 2020 in terms of spatial features. This gives a basis for the study of the Manasi region to formulate relevant land use development and rationally allocate land resources., (© 2023. The Author(s).)

Artificial intelligence (AI), or deep learning (DL), approaches have already found their way into our everyday lives. Furthermore, these methods are a central part of research in the life and natural sciences and have been applied in the form of machine learning for decades. In pharmaceutical and medicinal chemistry, and in computer-aided drug discovery, current developments are also changing the way drugs are developed. It is essential to familiarize students with AI methods already during their studies and prepare them for future tasks and challenges. We developed a set of interactive learning materials based on cheminformatics examples that can be used to establish such introductory AI courses in the life and natural sciences. These interactive notebooks are easily accessible without the need for installation, and no prior programming knowledge is required. Through these notebooks, students can easily study how AI/DL works and how these methods can be applied. This knowledge will foster a general competence when interacting with and evaluating future DL applications later in their career. The materials are freely available and publicly accessible through a GitHub repository in German and English., (© 2023 The Authors. Archiv der Pharmazie published by Wiley-VCH GmbH on behalf of Deutsche Pharmazeutische Gesellschaft.)

Purpose: Validation metrics are a key prerequisite for the reliable tracking of scientific progress and for deciding on the potential clinical translation of methods. While recent initiatives aim to develop comprehensive theoretical frameworks for understanding metric-related pitfalls in image analysis problems, there is a lack of experimental evidence on the concrete effects of common and rare pitfalls on specific applications. We address this gap in the literature in the context of colon cancer screening., Methods: Our contribution is twofold. Firstly, we present the winning solution of the Endoscopy Computer Vision Challenge on colon cancer detection, conducted in conjunction with the IEEE International Symposium on Biomedical Imaging 2022. Secondly, we demonstrate the sensitivity of commonly used metrics to a range of hyperparameters as well as the consequences of poor metric choices., Results: Based on comprehensive validation studies performed with patient data from six clinical centers, we found all commonly applied object detection metrics to be subject to high inter-center variability. Furthermore, our results clearly demonstrate that the adaptation of standard hyperparameters used in the computer vision community does not generally lead to the clinically most plausible results. Finally, we present localization criteria that correspond well to clinical relevance., Conclusion: We conclude from our study that (1) performance results in polyp detection are highly sensitive to various design choices, (2) common metric configurations do not reflect the clinical need and rely on suboptimal hyperparameters and (3) comparison of performance across datasets can be largely misleading. Our work could be a first step towards reconsidering common validation strategies in deep learning-based colonoscopy and beyond., (© 2023. The Author(s).)

Objectives: To develop a fully automated deep learning model for adrenal segmentation and to evaluate its performance in classifying adrenal hyperplasia., Methods: This retrospective study evaluated automated adrenal segmentation in 308 abdominal CT scans from 48 patients with adrenal hyperplasia and 260 patients with normal glands from 2010 to 2021 (mean age, 42 years; 156 women). The dataset was split into training, validation, and test sets at a ratio of 6:2:2. Contrast-enhanced CT images and manually drawn adrenal gland masks were used to develop a U-Net-based segmentation model. Predicted adrenal volumes were obtained by fivefold splitting of the dataset without overlapping the test set. Adrenal volumes and anthropometric parameters (height, weight, and sex) were utilized to develop an algorithm to classify adrenal hyperplasia, using multilayer perceptron, support vector classification, a random forest classifier, and a decision tree classifier. To measure the performance of the developed model, the dice coefficient and intraclass correlation coefficient (ICC) were used for segmentation, and area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were used for classification., Results: The model for segmenting adrenal glands achieved a Dice coefficient of 0.7009 for 308 cases and an ICC of 0.91 (95% CI, 0.90-0.93) for adrenal volume. The models for classifying hyperplasia had the following results: AUC, 0.98-0.99; accuracy, 0.948-0.961; sensitivity, 0.750-0.813; and specificity, 0.973-1.000., Conclusion: The proposed segmentation algorithm can accurately segment the adrenal glands on CT scans and may help clinicians identify possible cases of adrenal hyperplasia., Key Points: • A deep learning segmentation method can accurately segment the adrenal gland, which is a small organ, on CT scans. • The machine learning algorithm to classify adrenal hyperplasia using adrenal volume and anthropometric parameters (height, weight, and sex) showed good performance. • The proposed segmentation algorithm may help clinicians identify possible cases of adrenal hyperplasia., (© 2022. The Author(s), under exclusive licence to European Society of Radiology.)

Objective: The aim of this study was to quantify abdominal adiposity and generate data-driven adiposity subtypes with different diabetes risks., Methods: A total of 3817 participants from the Pinggu Metabolic Disease Study were recruited. A deep-learning-based recognition model on abdominal computed tomography (CT) images (A-CT model) was developed and validated in 100 randomly selected cases. The volumes and proportions of subcutaneous fat, visceral fat, liver fat, and muscle fat were automatically recognized in all cases. K-means clustering was used to identify subgroups using the proportions of the four fat components., Results: The Dice indices among the measurements assessed by the A-CT model and manual evaluation to detect liver fat, muscle fat, and subcutaneous fat areas were 0.96, 0.95, and 0.92, respectively. Three subtypes were generated separately in men and women: visceral fat dominant type (VFD); subcutaneous fat dominant type (SFD); and intermuscular fat dominant type (MFD). Compared with the SFD group, the MFD group had similar diabetes risk, and the VFD group had a 60% higher diabetes risk when age and BMI were adjusted for in men. The adjusted odds ratio for diabetes was 1.92 (95% CI: 1.32-2.78) in the MFD group and 6.14 (95% CI: 4.18-9.03) in the VFD group in women., Conclusions: This study identified gender-specific abdominal adiposity subgroups, which may help clinicians to distinguish diabetes risk quickly and automatically., (© 2023 The Obesity Society.)

The optoacoustic imaging (OAI) methods are rapidly evolving for resolving optical contrast in medical imaging applications. In practice, measurement strategies are commonly implemented under limited-view conditions due to oversized image objectives or system design limitations. Data acquired by limited-view detection may impart artifacts and distortions in reconstructed optoacoustic (OA) images. We propose a hybrid data-driven deep learning approach based on generative adversarial network (GAN), termed as LV-GAN, to efficiently recover high quality images from limited-view OA images. Trained on both simulation and experiment data, LV-GAN is found capable of achieving high recovery accuracy even under limited detection angles less than 60 ° . The feasibility of LV-GAN for artifact removal in biological applications was validated by ex vivo experiments based on two different OAI systems, suggesting high potential of a ubiquitous use of LV-GAN to optimize image quality or system design for different scanners and application scenarios., (© 2020 Wiley-VCH GmbH.)

Multiplex detection of protein post-translational modifications (PTMs), especially at point-of-care, is of great significance in cancer diagnosis. Herein, we report a machine learning-assisted photonic crystal hydrogel (PCH) sensor for multiplex detection of PTMs. With closely-related PCH sensors microfabricated on a single chip, our design achieved not only rapid screening of PTMs at specific protein sites by using only naked eyes/cellphone, but also the feasibility of real-time monitoring of phosphorylation reactions. By taking advantage of multiplex sensor chips and a neural network algorithm, accurate prediction of PTMs by both their types and concentrations was enabled. This approach was ultimately used to detect and differentiate up/down regulation of different phosphorylation sites within the same protein in live mammalian cells. Our developed method thus holds potential for POC identification of various PTMs in early-stage diagnosis of protein-related diseases., (© 2023 Wiley-VCH GmbH.)

Cancer management is major concern of health organizations and viral cancers account for approximately 15.4% of all known human cancers. Due to large number of patients, efficient treatments for viral cancers are needed. De novo drug discovery is time consuming and expensive process with high failure rate in clinical stages. To address this problem and provide treatments to patients suffering from viral cancers faster, drug repurposing emerges as an effective alternative which aims to find the other indications of the Food and Drug Administration approved drugs. Applied to viral cancers, drug repurposing studies following the niche have tried to find if already existing drugs could be used to treat viral cancers. Multiple drug repurposing approaches till date have been introduced with successful results in viral cancers and many drugs have been successfully repurposed various viral cancers. Here in this study, a critical review of viral cancer related databases, tools, and different machine learning, deep learning and virtual screening-based drug repurposing studies focusing on viral cancers is provided. Additionally, the mechanism of viral cancers is presented along with drug repurposing case study specific to each viral cancer. Finally, the limitations and challenges of various approaches along with possible solutions are provided., (© 2023 Wiley Periodicals LLC.)

The complex shape of the foot, consisting of 26 bones, variable ligaments, tendons, and muscles leads to misdiagnosis of foot fractures. Despite the introduction of artificial intelligence (AI) to diagnose fractures, the accuracy of foot fracture diagnosis is lower than that of conventional methods. We developed an AI assistant system that assists with consistent diagnosis and helps interns or non-experts improve their diagnosis of foot fractures, and compared the effectiveness of the AI assistance on various groups with different proficiency. Contrast-limited adaptive histogram equalization was used to improve the visibility of original radiographs and data augmentation was applied to prevent overfitting. Preprocessed radiographs were fed to an ensemble model of a transfer learning-based convolutional neural network (CNN) that was developed for foot fracture detection with three models: InceptionResNetV2, MobilenetV1, and ResNet152V2. After training the model, score class activation mapping was applied to visualize the fracture based on the model prediction. The prediction result was evaluated by the receiver operating characteristic (ROC) curve and its area under the curve (AUC), and the F1-Score. Regarding the test set, the ensemble model exhibited better classification ability (F1-Score: 0.837, AUC: 0.95, Accuracy: 86.1%) than other single models that showed an accuracy of 82.4%. With AI assistance for the orthopedic fellow, resident, intern, and student group, the accuracy of each group improved by 3.75%, 7.25%, 6.25%, and 7% respectively and diagnosis time was reduced by 21.9%, 14.7%, 24.4%, and 34.6% respectively., (© 2023. Australasian College of Physical Scientists and Engineers in Medicine.)

Intraoperative guidance tools for thyroid surgery based on optical coherence tomography (OCT) could aid distinguish between normal and diseased tissue. However, OCT images are difficult to interpret, thus, real-time automatic analysis could support the clinical decision-making. In this study, several deep learning models were investigated for thyroid disease classification on 2D and 3D OCT data obtained from ex vivo specimens of 22 patients undergoing surgery and diagnosed with several thyroid pathologies. Additionally, two open-access datasets were used to evaluate the custom models. On the thyroid dataset, the best performance was achieved by the 3D vision transformer model with a Matthew's correlation coefficient (MCC) of 0.79 (accuracy = 0.90) for the normal-versus-abnormal classification. On the open-access datasets, the custom models achieved the best performance (MCC > 0.88, accuracy > 0.96). Results obtained for the normal-versus-abnormal classification suggest OCT, complemented with deep learning-based analysis, as a tool for real-time automatic diseased tissue identification in thyroid surgery., (© 2022 The Authors. Journal of Biophotonics published by Wiley-VCH GmbH.)

Diagnosis and detection of progression of glaucoma remains challenging. Artificial intelligence-based tools have the potential to improve and standardize the assessment of glaucoma but development of these algorithms is difficult given the multimodal and variable nature of the diagnosis. Currently, most algorithms are focused on a single imaging modality, specifically screening and diagnosis based on fundus photos or optical coherence tomography images. Use of anterior segment optical coherence tomography and goniophotographs is limited. The majority of algorithms designed for disease progression prediction are based on visual fields. No studies in our literature search assessed the use of artificial intelligence for treatment response prediction and no studies conducted prospective testing of their algorithms. Additional challenges to the development of artificial intelligence-based tools include scarcity of data and a lack of consensus in diagnostic criteria. Although research in the use of artificial intelligence for glaucoma is promising, additional work is needed to develop clinically usable tools., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2023 Asia-Pacific Academy of Ophthalmology. Published by Wolters Kluwer Health, Inc. on behalf of the Asia-Pacific Academy of Ophthalmology.)

With the continuous integration of deep learning and the technique of molecular biology, target detection models must accurately detect the position of each cell in the image and classify it correctly. We present a model for the multi-scale feature fusion of the existing human cell image dataset based on Gaussian mixedly clustering. First, a novel feature extraction network for extracting preliminary features at picture multi scales was presented, which was based on a residual neural network with Instance Normalization and a Mish activation function. Second, the presented model adopts the idea of feature fusion and introduced a new type of feature fusion network to integrate feature graphs on different scales. Furthermore, a Gaussian hybrid clustering algorithm was proposed to cluster the hyperparameters. Based on the experimental results, the average accuracy of the proposed model in the human cell image dataset exceeds 0.96, which improves by 11.9% compared with the existing target detection methods in the same field. Experiments show that the proposed model had been adapted to datasets with uneven sample distribution, providing new ideas for research on medical images., (© 2022 Wiley-VCH GmbH.)

Interatomic potentials (IAPs), which describe the potential energy surface of atoms, are a fundamental input for atomistic simulations. However, existing IAPs are either fitted to narrow chemistries or too inaccurate for general applications. Here we report a universal IAP for materials based on graph neural networks with three-body interactions (M3GNet). The M3GNet IAP was trained on the massive database of structural relaxations performed by the Materials Project over the past ten years and has broad applications in structural relaxation, dynamic simulations and property prediction of materials across diverse chemical spaces. About 1.8 million materials from a screening of 31 million hypothetical crystal structures were identified to be potentially stable against existing Materials Project crystals based on M3GNet energies. Of the top 2,000 materials with the lowest energies above the convex hull, 1,578 were verified to be stable using density functional theory calculations. These results demonstrate a machine learning-accelerated pathway to the discovery of synthesizable materials with exceptional properties., (© 2022. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Aim: To investigate the feasibility of predicting dental implant loss risk with deep learning (DL) based on preoperative cone-beam computed tomography., Materials and Methods: Six hundred and three patients who underwent implant surgery (279 high-risk patients who did and 324 low-risk patients who did not experience implant loss within 5 years) between January 2012 and January 2020 were enrolled. Three models, a logistic regression clinical model (CM) based on clinical features, a DL model based on radiography features, and an integrated model (IM) developed by combining CM with DL, were developed to predict the 5-year implant loss risk. The area under the receiver operating characteristic curve (AUC) was used to evaluate the model performance. Time to implant loss was considered for both groups, and Kaplan-Meier curves were created and compared by the log-rank test., Results: The IM exhibited the best performance in predicting implant loss risk (AUC = 0.90, 95% confidence interval [CI] 0.84-0.95), followed by the DL model (AUC = 0.87, 95% CI 0.80-0.92) and the CM (AUC = 0.72, 95% CI 0.63-0.79)., Conclusions: Our study offers preliminary evidence that both the DL model and the IM performed well in predicting implant fate within 5 years and thus may greatly facilitate implant practitioners in assessing preoperative risks., (© 2022 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Purpose: Computer-aided automatic pancreas segmentation is essential for early diagnosis and treatment of pancreatic diseases. However, the annotation of pancreas images requires professional doctors and considerable expenditure. Due to imaging differences among various institution population, scanning devices, imaging protocols, and so on, significant degradation in the performance of model inference results is prone to occur when models trained with domain-specific (usually institution-specific) datasets are directly applied to new (other centers/institutions) domain data. In this paper, we propose a novel unsupervised domain adaptation method based on adversarial learning to address pancreas segmentation challenges with the lack of annotations and domain shift interference., Methods: A 3D semantic segmentation model with attention module and residual module is designed as the backbone pancreas segmentation model. In both segmentation model and domain adaptation discriminator network, a multiscale progressively weighted structure is introduced to acquire different field of views. Features of labeled data and unlabeled data are fed in pairs into the proposed multiscale discriminator to learn domain-specific characteristics. Then the unlabeled data features with pseudodomain label are fed to the discriminator to acquire domain-ambiguous information. With this adversarial learning strategy, the performance of the segmentation network is enhanced to segment unseen unlabeled data., Results: Experiments were conducted on two public annotated datasets as source datasets, respectively, and one private dataset as target dataset, where annotations were not used for the training process but only for evaluation. The 3D segmentation model achieves comparative performance with state-of-the-art pancreas segmentation methods on source domain. After implementing our domain adaptation architecture, the average dice similarity coefficient (DSC) of the segmentation model trained on the NIH-TCIA source dataset increases from 58.79% to 72.73% on the local hospital dataset, while the performance of the target domain segmentation model transferred from the medical segmentation decathlon (MSD) source dataset rises from 62.34% to 71.17%., Conclusions: Correlations of features across data domains are utilized to train the pancreas segmentation model on unlabeled data domain, improving the generalization of the model. Our results demonstrate that the proposed method enables the segmentation model to make meaningful segmentation for unseen data of the training set. In the future, the proposed method has the potential to apply segmentation model trained on public dataset to clinical unannotated CT images from local hospital, effectively assisting radiologists in clinical practice., (© 2022 American Association of Physicists in Medicine.)

Purpose: Many children who undergo MR of the knee to evaluate traumatic injury may not undergo a separate dedicated evaluation of their skeletal maturity, and we wished to investigate how accurately skeletal maturity could be automatically inferred from knee MRI using deep learning to offer this additional information to clinicians., Materials and Methods: Retrospective data from 894 studies from 783 patients were obtained (mean age 13.1 years, 47% female). Coronal and sagittal sequences that were T1/PD-weighted were included and resized to 224 × 224 pixels. Data were divided into train (n = 673), tune (n = 48), and test (n = 173) sets, and children were separated across sets. The chronologic age was predicted using deep learning approaches based on a long short-term memory (LSTM) model, which took as input DenseNet-121-extracted features from all T1/PD coronal and sagittal slices. Each test case was manually assigned a bone age by two radiology residents using a reference atlas provided by Pennock and Bomar. The patient's age served as ground truth., Results: The error of the model's predictions for chronological age was not significantly different from that of radiology residents (model M.S.E. 1.30 vs. resident 0.99, paired t-test = 1.47, p = 0.14). Pearson correlation between model and resident prediction of chronologic age was 0.96 (p < 0.001)., Conclusion: A deep learning-based approach demonstrated ability to infer skeletal maturity from knee MR sequences that was not significantly different from resident performance and did so in less than 2% of the time required by a human expert. This may offer a method for automatically evaluating lower extremity skeletal maturity automatically as part of every MR examination., (© 2022. ISS.)

Background and Aims: Chronic hepatitis B virus (CHB) infection remains a major global health burden and the non-invasive and accurate diagnosis of significant liver fibrosis (≥ F2) in CHB patients is clinically very important. This study aimed to assess the potential of the joint use of ultrasound images of liver parenchyma, liver stiffness values, and patients' clinical parameters in a deep learning model to improve the diagnosis of ≥ F2 in CHB patients., Methods: Of 527 CHB patients who underwent US examination, liver elastography and biopsy, 284 eligible patients were included. We developed a deep learning-based data integration network (DI-Net) to fuse the information of ultrasound images of liver parenchyma, liver stiffness values and patients' clinical parameters for diagnosing ≥ F2 in CHB patients. The performance of DI-Net was cross-validated in a main cohort (n = 155) of the included patients and externally validated in an independent cohort (n = 129), with comparisons against single-source data-based models and other non-invasive methods in terms of the area under the receiver-operating-characteristic curve (AUC)., Results: DI-Net achieved an AUC of 0.943 (95% confidence interval [CI] 0.893-0.973) in the cross-validation, and an AUC of 0.901 (95% CI 0.834-0.945) in the external validation, which were significantly greater than those of the comparative methods (AUC ranges: 0.774-0.877 and 0.741-0.848 for cross- and external validations, respectively, p s  < 0.01)., Conclusion: The joint use of ultrasound images of liver parenchyma, liver stiffness values, and patients' clinical parameters in a deep learning model could significantly improve the diagnosis of ≥ F2 in CHB patients., (© 2022. Asian Pacific Association for the Study of the Liver.)

Histopathologic evaluation of muscle biopsy samples is essential for classifying and diagnosing muscle diseases. However, the numbers of experienced specialists and pathologists are limited. Although new technologies such as artificial intelligence are expected to improve medical reach, their use with rare diseases, such as muscle diseases, is challenging because of the limited availability of training datasets. To address this gap, we developed an algorithm based on deep convolutional neural networks (CNNs) and collected 4041 microscopic images of 1400 hematoxylin-and-eosin-stained pathology slides stored in the National Center of Neurology and Psychiatry for training CNNs. Our trained algorithm differentiated idiopathic inflammatory myopathies (mostly treatable) from hereditary muscle diseases (mostly non-treatable) with an area under the curve (AUC) of 0.996 and achieved better sensitivity and specificity than the diagnoses done by nine physicians under limited diseases and conditions. Furthermore, it successfully and accurately classified four subtypes of the idiopathic inflammatory myopathies with an average AUC of 0.958 and classified seven subtypes of hereditary muscle disease with an average AUC of 0.936. We also established a method to validate the similarity between the predictions made by the algorithm and the seven physicians using visualization technology and clarified the validity of the predictions. These results support the reliability of the algorithm and suggest that our algorithm has the potential to be used straightforwardly in a clinical setting., (© 2021. The Author(s), under exclusive licence to United States and Canadian Academy of Pathology.)

Wetlands are critical to the ecology because they maintain biodiversity and provide home for a variety of species. Researching, mapping, and conservation of wetlands is a challenging and time-consuming process. Because they produce temporal and geographical information, remote sensing and photogrammetric approaches are useful tools for analyzing and managing wetlands. In this study, the water areas of five different wetlands obtained with Sentinel-2 images in Turkey were classified. Although obtaining large amounts of high-dimensional dataset labeled for various land types is costly, it is a significant advantage to use it after model training in a wide range of applications. In this paper, the EuroSAT dataset was used in the validation process. Proposed deep learning-based 1D convolutional neural networks (CNN) and traditional machine learning methods (i.e., support vector machine, linear discriminant analysis, K-nearest neighborhood, canonical correlation forests, and AdaBoost.M1) were compared quantitatively (i.e., accuracy, recall, precision, specificity, F-score, and image quality assessment metrics) and qualitatively. Finally, pairwise comparison was made with chi-square-based McNemar's test. There is a statistical difference between 1D CNN and machine learning method (except the support vector machine vs linear discriminant analysis in Test 1 area). CNN models outperform machine learning algorithms in terms of non-linear function approximation and the ability to extract and articulate data features. Since 1D CNNs can process data in a highly complex and unique feature space, they are very successful in segmenting strongly related and highly correlated discrete signals. It also has advantages over machine learning methods for water body extraction in that it can be integrated with sophisticated image pre-processing and standardization tools, is less susceptible to low-level random noise, and provides shift in variations and contrast-invariant image local transforms., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Posttranslational modification (PTM ) is a ubiquitous phenomenon in both eukaryotes and prokaryotes which gives rise to enormous proteomic diversity. PTM mostly comes in two flavors: covalent modification to polypeptide chain and proteolytic cleavage. Understanding and characterization of PTM is a fundamental step toward understanding the underpinning of biology. Recent advances in experimental approaches, mainly mass-spectrometry-based approaches, have immensely helped in obtaining and characterizing PTMs. However, experimental approaches are not enough to understand and characterize more than 450 different types of PTMs and complementary computational approaches are becoming popular. Recently, due to the various advancements in the field of Deep Learning (DL), along with the explosion of applications of DL to various fields, the field of computational prediction of PTM has also witnessed the development of a plethora of deep learning (DL)-based approaches. In this book chapter, we first review some recent DL-based approaches in the field of PTM site prediction. In addition, we also review the recent advances in the not-so-studied PTM , that is, proteolytic cleavage predictions. We describe advances in PTM prediction by highlighting the Deep learning architecture, feature encoding, novelty of the approaches, and availability of the tools/approaches. Finally, we provide an outlook and possible future research directions for DL-based approaches for PTM prediction., (© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)