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Accurate segmentation of cardiac structures in magnetic resonance imaging (MRI) is essential for reliable diagnosis and management of cardiovascular disease. Although numerous robust models have been proposed, no single segmentation model consistently outperforms others across all cases, and models that excel on one dataset may not achieve similar accuracy on others or when the same dataset is expanded. This study introduces FCTransNet, an ensemble-based computer-aided diagnosis system that leverages the complementary strengths of Vision Transformer (ViT) models (specifically TransUNet, SwinUNet, and SegFormer) to address these challenges. To achieve this, we propose a novel pixel-level fusion technique, the Intelligent Weighted Summation Technique (IWST), which reconstructs the final segmentation mask by integrating the outputs of the ViT models and accounting for their diversity. First, a dedicated U-Net module isolates the region of interest (ROI) from cine MRI images, which is then processed by each ViT to generate preliminary segmentation masks. The IWST subsequently fuses these masks to produce a refined final segmentation. By using a local window around each pixel, IWST captures specific neighborhood details while incorporating global context to enhance segmentation accuracy. Experimental validation on the ACDC dataset shows that FCTransNet significantly outperforms individual ViTs and other deep learning-based methods, achieving a Dice Score (DSC) of 0.985 and a mean Intersection over Union (IoU) of 0.914 in the end-diastolic phase. In addition, FCTransNet maintains high accuracy in the end-systolic phase with a DSC of 0.989 and an IoU of 0.908. These results underscore FCTransNet's ability to improve cardiac MRI segmentation accuracy. [ABSTRACT FROM AUTHOR]

Objectives: This study aims to externally validate a commercially available Computer-Aided Detection (CAD)-system for the automatic detection and characterization of solid, part-solid, and ground-glass lung nodules (LN) on CT scans. Methods: This retrospective study encompasses 263 chest CT scans performed between January 2020 and December 2021 at a Dutch university hospital. All scans were read by a radiologist (R1) and compared with the initial radiology report. Conflicting scans were assessed by an adjudicating radiologist (R2). All scans were also processed by CAD. The standalone performance of CAD in terms of sensitivity and false-positive (FP)-rate for detection was calculated together with the sensitivity for characterization, including texture, calcification, speculation, and location. The R1's detection sensitivity was also assessed. Results: A total of 183 true nodules were identified in 121 nodule-containing scans (142 non-nodule-containing scans), of which R1 identified 165/183 (90.2%). CAD detected 149 nodules, of which 12 were not identified by R1, achieving a sensitivity of 149/183 (81.4%) with an FP-rate of 49/121 (0.405). CAD's detection sensitivity for solid, part-solid, and ground-glass LNs was 82/94 (87.2%), 42/47 (89.4%), and 25/42 (59.5%), respectively. The classification accuracy for solid, part-solid, and ground-glass LNs was 81/82 (98.8%), 16/42 (38.1%), and 18/25 (72.0%), respectively. Additionally, CAD demonstrated overall classification accuracies of 137/149 (91.9%), 123/149 (82.6%), and 141/149 (94.6%) for calcification, spiculation, and location, respectively. Conclusions: Although the overall detection rate of this system slightly lags behind that of a radiologist, CAD is capable of detecting different LNs and thereby has the potential to enhance a reader's detection rate. While promising characterization performances are obtained, the tool's performance in terms of texture classification remains a subject of concern. Clinical relevance statement: Numerous lung nodule computer-aided detection-systems are commercially available, with some of them solely being externally validated based on their detection performance on solid nodules. We encourage researchers to assess performances by incorporating all relevant characteristics, including part-solid and ground-glass nodules. Key Points: Few computer-aided detection (CAD) systems are externally validated for automatic detection and characterization of lung nodules. A detection sensitivity of 81.4% and an overall texture classification sensitivity of 77.2% were measured utilizing CAD. CAD has the potential to increase single reader detection rate, however, improvement in texture classification is required. [ABSTRACT FROM AUTHOR]

Recent advancements in deep learning have significantly improved the intelligent classification of gastrointestinal (GI) diseases, particularly in aiding clinical diagnosis. This paper seeks to review a computer-aided diagnosis (CAD) system for GI diseases, aligning with the actual clinical diagnostic process. It offers a comprehensive survey of deep learning (DL) techniques tailored for classifying GI diseases, addressing challenges inherent in complex scenes, clinical constraints, and technical obstacles encountered in GI imaging. Firstly, the esophagus, stomach, small intestine, and large intestine were located to determine the organs where the lesions were located. Secondly, location detection and classification of a single disease are performed on the premise that the organ's location corresponding to the image is known. Finally, comprehensive classification for multiple diseases is carried out. The results of single and multi-classification are compared to achieve more accurate classification outcomes, and a more effective computer-aided diagnosis system for gastrointestinal diseases was further constructed. [ABSTRACT FROM AUTHOR]

Children who do not sufficiently develop graphomotor skills essential for handwriting often develop graphomotor disabilities (GD), impacting the self-esteem and academic performance of the individual. Current examination methods of GD consist of scales and questionaries, which lack objectivity, rely on the perceptual abilities of the examiner, and may lead to inadequately targeted remediation. Nowadays, one way to address the factor of subjectivity is to incorporate supportive machine learning (ML) based assessment. However, even with the increasing popularity of decision-support systems facilitating the diagnosis and assessment of GD, this field still lacks an understanding of deficient kinematics concerning the direction of pen movement. This study aims to explore the impact of movement direction on the manifestations of graphomotor difficulties in school-aged. We introduced a new fractional-order derivative-based approach enabling quantification of kinematic aspects of handwriting concerning the direction of movement using polar plot representation. We validated the novel features in a barrage of machine learning scenarios, testing various training methods based on extreme gradient boosting trees (XGBboost), Bayesian, and random search hyperparameter tuning methods. Results show that our novel features outperformed the baseline and provided a balanced accuracy of 87 % (sensitivity = 82 %, specificity = 92 %), performing binary classification (children with/without graphomotor difficulties). The final model peaked when using only 43 out of 250 novel features, showing that XGBoost can benefit from feature selection methods. Proposed features provide additional information to an automated classifier with the potential of human interpretability thanks to the possibility of easy visualization using polar plots. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the most aggressive types of cancer, and its early diagnosis is crucial for reducing mortality rates and ensuring timely treatment. Computer-aided diagnosis systems provide automated mammography image processing, interpretation, and grading. However, since the currently existing methods suffer from such issues as overfitting, lack of adaptability, and dependence on massive annotated datasets, the present work introduces a hybrid approach to enhance breast cancer classification accuracy. The proposed Q-BGWO-SQSVM approach utilizes an improved quantum-inspired binary Grey Wolf Optimizer and combines it with SqueezeNet and Support Vector Machines to exhibit sophisticated performance. SqueezeNet's fire modules and complex bypass mechanisms extract distinct features from mammography images. Then, these features are optimized by the Q-BGWO for determining the best SVM parameters. Since the current CAD system is more reliable, accurate, and sensitive, its application is advantageous for healthcare. The proposed Q-BGWO-SQSVM was evaluated using diverse databases: MIAS, INbreast, DDSM, and CBIS-DDSM, analyzing its performance regarding accuracy, sensitivity, specificity, precision, F1 score, and MCC. Notably, on the CBIS-DDSM dataset, the Q-BGWO-SQSVM achieved remarkable results at 99% accuracy, 98% sensitivity, and 100% specificity in 15-fold cross-validation. Finally, it can be observed that the performance of the designed Q-BGWO-SQSVM model is excellent, and its potential realization in other datasets and imaging conditions is promising. The novel Q-BGWO-SQSVM model outperforms the state-of-the-art classification methods and offers accurate and reliable early breast cancer detection, which is essential for further healthcare development. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) constitutes a fatal neurodegenerative disorder and represents the most prevalent form of dementia among the elderly population. Traditional manual AD classification methods, such as clinical diagnosis, are known to be time-consuming and labor-intensive, with relatively low accuracy. Therefore, our work aims to develop a new deep learning framework to tackle this challenge. Our proposed model integrates ConvNeXt with three-dimensional (3D) convolution and incorporates a 3D Squeeze-and-Excitation (3D-SE) attention mechanism to enhance early classification of AD. The experimental data is sourced from the publicly accessible Alzheimer's disease Neuroimaging Initiative (ADNI) database, with raw Magnetic Resonance Imaging (MRI) data preprocessed using SPM12 software. Subsequently, the preprocessed data is input into the 3D-SEConvNeXt network to perform four classification tasks: distinguishing between AD and Normal Control (NC), Mild Cognitive Impairment (MCI) and NC, AD and MCI, as well as AD, MCI, and NC. The experimental results indicate that the 3D-SEConvNeXt model consistently outperforms alternative models in terms of accuracy, achieving commendable outcomes in early AD diagnostic tasks. [ABSTRACT FROM AUTHOR]

Primary malignant bone tumors are the third highest cause of cancer-related mortality among patients under the age of 20. X-ray scan is the primary tool for detecting bone tumors. However, due to the varying morphologies of bone tumors, it is challenging for radiologists to make a definitive diagnosis based on radiographs. With the recent advancement in deep learning algorithms, there is a surge of interest in computer-aided diagnosis of primary bone tumors. Nonetheless, the development in this field has been hindered by the lack of publicly available X-ray datasets for bone tumors. To tackle this challenge, we established the Bone Tumor X-ray Radiograph dataset (termed BTXRD) in collaboration with multiple medical institutes and hospitals. The BTXRD dataset comprises 3,746 bone images (1,879 normal and 1,867 tumor), with clinical information and global labels available for each image, and distinct mask and annotated bounding box for each tumor instance. This publicly available dataset can support the development and evaluation of deep learning algorithms for the diagnosis of primary bone tumors. [ABSTRACT FROM AUTHOR]

This study investigates the effectiveness of deep learning models in diagnosing cardiac amyloidosis using 99mTc-PYP scintigraphy. We evaluated more than 40 deep learning models, including both convolutional neural networks (CNNs) and Vision Transformer (ViT) models. The highest-performing model achieved 89.80% accuracy. The study highlights the potential of deep learning methods to improve diagnostic accuracy and reduce patient wait times. These results demonstrate the clinical value of deep learning models in early and accurate cardiac amyloidosis diagnosis, contributing to better patient outcomes and timely interventions. [ABSTRACT FROM AUTHOR]

The last decades have brought an interest in ultrasound applications in dermatology. Especially in the case of atopic dermatitis, where the formation of a subepidermal low echogenic band (SLEB) may serve as an independent indicator of the effects of treatment, the use of ultrasound is of particular interest. This study proposes and evaluates the computer-aided diagnosis method for assessing atopic dermatitis (AD). The fully automated image processing framework combines advanced machine learning techniques for fast, reliable, and repeatable HFUS image analysis, supporting clinical decisions. The proposed methodology comprises accurate SLEB segmentation followed by a classification step. The data set includes 20 MHz images of 80 patients diagnosed with AD according to Hanifin and Rajka criteria, which were evaluated before and after treatment. The ground true labels- clinical evaluation based on Investigator Global Assessment index (IGA score) together with ultrasound skin examination was performed. For reliable analysis, in further experiments, two experts annotated the HFUS images twice in two-week intervals. The analysis aimed to verify whether the fully automated method can classify the HFUS images at the expert level. The Dice coefficient values for segmentation reached 0.908 for SLEB and 0.936 for the entry echo layer. The accuracy of SLEB presence detection results (IGA0) is equal to 98% and slightly outperforms the experts' assessment, which reaches 96%. The overall accuracy of the AD assessment was equal to 69% (Cohen's kappa 0.78) and was comparable with the experts' assessment, ranging between 64% and 70% (Cohen's kappa 0.73–0.79). The results indicate that the automated method can be applied to AD assessment, and its combination with standard diagnosis may benefit repeatable analysis and a better understanding of the processes that take place within the skin and aid treatment monitoring. [ABSTRACT FROM AUTHOR]

Atherosclerosis causes heart disease by forming plaques in arterial walls. IVUS imaging provides a high-resolution cross-sectional view of coronary arteries and plaque morphology. Healthcare professionals diagnose and quantify atherosclerosis physically or using VH-IVUS software. Since manual or VH-IVUS software-based diagnosis is time-consuming, automated plaque characterization tools are essential for accurate atherosclerosis detection and classification. Recently, deep learning (DL) and computer vision (CV) approaches are promising tools for automatically classifying plaques on IVUS images. With this motivation, this manuscript proposes an automated atherosclerotic plaque classification method using a hybrid Ant Lion Optimizer with Deep Learning (AAPC-HALODL) technique on IVUS images. The AAPC-HALODL technique uses the faster regional convolutional neural network (Faster RCNN)-based segmentation approach to identify diseased regions in the IVUS images. Next, the ShuffleNet-v2 model generates a useful set of feature vectors from the segmented IVUS images, and its hyperparameters can be optimally selected by using the HALO technique. Finally, an average ensemble classification process comprising a stacked autoencoder (SAE) and deep extreme learning machine (DELM) model can be utilized. The MICCAI Challenge 2011 dataset was used for AAPC-HALODL simulation analysis. A detailed comparative study showed that the AAPC-HALODL approach outperformed other DL models with a maximum accuracy of 98.33%, precision of 97.87%, sensitivity of 98.33%, and F score of 98.10%. [ABSTRACT FROM AUTHOR]

Part 2 explores the transformative potential of artificial intelligence (AI) in addressing the complexities of headache disorders through innovative approaches, including digital twin models, wearable healthcare technologies and biosensors, and AI-driven drug discovery. Digital twins, as dynamic digital representations of patients, offer opportunities for personalized headache management by integrating diverse datasets such as neuroimaging, multiomics, and wearable sensor data to advance headache research, optimize treatment, and enable virtual trials. In addition, AI-driven wearable devices equipped with next-generation biosensors combined with multi-agent chatbots could enable real-time physiological and biochemical monitoring, diagnosing, facilitating early headache attack forecasting and prevention, disease tracking, and personalized interventions. Furthermore, AI-driven advances in drug discovery leverage machine learning and generative AI to accelerate the identification of novel therapeutic targets and optimize treatment strategies for migraine and other headache disorders. Despite these advances, challenges such as data standardization, model explainability, and ethical considerations remain pivotal. Collaborative efforts between clinicians, biomedical and biotechnological engineers, AI scientists, legal representatives and bioethics experts are essential to overcoming these barriers and unlocking AI's full potential in transforming headache research and healthcare. This is a call to action in proposing novel frameworks for integrating AI-based technologies into headache care. [ABSTRACT FROM AUTHOR]

Purpose: Automated prostate disease classification on multi-parametric MRI has recently shown promising results with the use of convolutional neural networks (CNNs). The vision transformer (ViT) is a convolutional free architecture which only exploits the self-attention mechanism and has surpassed CNNs in some natural imaging classification tasks. However, these models are not very robust to textural shifts in the input space. In MRI, we often have to deal with textural shift arising from varying acquisition protocols. Here, we focus on the ability of models to generalise well to new magnet strengths for MRI. Method: We propose a new framework to improve the robustness of vision transformer-based models for disease classification by constructing discrete representations of the data using vector quantisation. We sample a subset of the discrete representations to form the input into a transformer-based model. We use cross-attention in our transformer model to combine the discrete representations of T2-weighted and apparent diffusion coefficient (ADC) images. Results: We analyse the robustness of our model by training on a 1.5 T scanner and test on a 3 T scanner and vice versa. Our approach achieves SOTA performance for classification of lesions on prostate MRI and outperforms various other CNN and transformer-based models in terms of robustness to domain shift and perturbations in the input space. Conclusion: We develop a method to improve the robustness of transformer-based disease classification of prostate lesions on MRI using discrete representations of the T2-weighted and ADC images. [ABSTRACT FROM AUTHOR]

Deep learning analysis of electrocardiography (ECG) may predict cardiovascular outcomes. We present a novel multi-task deep learning model, the ECG-MACE, which predicts the one-year first-ever major adverse cardiovascular events (MACE) using 2,821,889 standard 12-lead ECGs, including training (n = 984,895), validation (n = 422,061), and test (n = 1,414,933) sets, from Chang Gung Memorial Hospital database in Taiwan. Data from another independent medical center (n = 113,224) was retrieved for external validation. The model's performance achieves AUROCs of 0.90 for heart failure (HF), 0.85 for myocardial infarction (MI), 0.76 for ischemic stroke (IS), and 0.89 for mortality. Furthermore, it outperforms the Framingham risk score at 5-year MACEs and 10-year mortality prediction. Over 10-year follow-ups, the model-predicted-positive group exhibits significantly higher MACE incidences than the model-predicted-negative group (relative incidence ratio: HF: 15.28; MI: 7.87; IS: 4.74; mortality: 13.18). Using solely ECGs, ECG-MACE effectively predicts one-year events and exhibits long-term anticipation. It provides potential applications in preventive medicine. [ABSTRACT FROM AUTHOR]

Chimp optimization algorithm (CHOA) is a recently developed nature-inspired technique that mimics the swarm intelligence of chimpanzee colonies. However, the original CHOA suffers from slow convergence and a tendency to reach local optima when dealing with multidimensional problems. To address these limitations, we propose TASR-CHOA, a twofold adaptive stochastic reinforced variant. The TASR-CHOA algorithm integrates two novel methodologies: a stochastic approach to improve the speed at which convergence is achieved and a dual adaptive weighting approach to optimize the exploration of early patterns, which refer to initial trends or behaviors in the algorithm's convergence process during the early stages of iterations and the exploitation of subsequent tendencies, indicating how these initial trends develop over time as the algorithm iterates and refines its search. To evaluate TASR-CHOA, we apply it to 29 conventional optimization benchmark functions, 10 IEEE CEC-06 benchmarks, 30 complicated IEEE CEC-BC benchmark functions, and ten well-known benchmark real-world challenges. We evaluate TASR-CHOA against 4 categorical optimization techniques as well as 18 top IEEE CEC-BC algorithms. Based on our broad investigation done using three statistical tests, we claim that TASR-CHOA outperforms the majority of the algorithms since within a position takes the best place, 54 out of 73 evaluation functions and engineering problems. In other cases, the results are almost the same as those of SHADE and CMA-ES over several comparisons. As an illustrative application of this joint approach, a computer-aided fire detection task is performed using a deep convolutional neural network combined with TASR-CHOA. We also outline the algorithm executed in steps, indicating computational complexity, which is O(NI×NV) + O(NI×NV×6) + O(NI×NV + 2×NI×NV) as a function of number of individuals (NI) and dimensions (NV). [ABSTRACT FROM AUTHOR]

This study evaluated the impact of differing false positive (FP) rates in two computer-aided detection (CADe) systems on the clinical effectiveness of artificial intelligence (AI)-assisted colonoscopy. The primary outcomes were adenoma detection rate (ADR) and adenomas per colonoscopy (APC). The ADR in the control, system A (3.2% FP rate), and system B (0.6% FP rate) groups were 44.3%, 43.4%, and 50.4%, respectively, with system B showing a significantly higher ADR than the control group. The APC for the control, A, and B groups were 0.75, 0.83, and 0.90, respectively, with system B also showing a higher APC than the control. The non-true lesion resection rates were 23.8%, 29.2%, and 21.3%, with system B having the lowest. The system with lower FP rates demonstrated improved ADR and APC without increasing the resection of non-neoplastic lesions. These findings suggest that higher FP rates negatively affect the clinical performance of AI-assisted colonoscopy. [ABSTRACT FROM AUTHOR]

Image segmentation is the process of splitting a digital image into distinct segments or categories based on shared characteristics like texture, color, and intensity. Its primary aim is to simplify the image for easier analysis while preserving its important features. Each pixel in the image is assigned a label, grouped together by pixels with similar traits together. Segmentation helps to delineate boundaries and identify objects such as curves or lines within the image. The process generates a series of segmented images that cover the entire original image. This article reviews emerging applications of image segmentation in medical diagnostics, specifically employing nature-inspired optimization algorithms (NIOAs). It begins by outlining different segmentation methods and NIOAs types, then by examining relevant databases and medical imaging technologies. The study draws on a diverse range of research sources. Finally, this paper briefly discusses the challenges and future trends of medical image segmentation using NIOAs to detect different diseases. [ABSTRACT FROM AUTHOR]

In computer-aided diagnosis tools employed for skin cancer treatment and early diagnosis, skin lesion segmentation is important. However, achieving precise segmentation is challenging due to inherent variations in appearance, contrast, texture, and blurry lesion boundaries. This research presents a robust approach utilizing a dilated convolutional residual network, which incorporates an attention-based spatial feature enhancement block (ASFEB) and employs a guided decoder strategy. In each dilated convolutional residual block, dilated convolution is employed to broaden the receptive field with varying dilation rates. To improve the spatial feature information of the encoder, we employed an attention-based spatial feature enhancement block in the skip connections. The ASFEB in our proposed method combines feature maps obtained from average and maximum-pooling operations. These combined features are then weighted using the active outcome of global average pooling and convolution operations. Additionally, we have incorporated a guided decoder strategy, where each decoder block is optimized using an individual loss function to enhance the feature learning process in the proposed AD-Net. The proposed AD-Net presents a significant benefit by necessitating fewer model parameters compared to its peer methods. This reduction in parameters directly impacts the number of labeled data required for training, facilitating faster convergence during the training process. The effectiveness of the proposed AD-Net was evaluated using four public benchmark datasets. We conducted a Wilcoxon signed-rank test to verify the efficiency of the AD-Net. The outcomes suggest that our method surpasses other cutting-edge methods in performance, even without the implementation of data augmentation strategies. [ABSTRACT FROM AUTHOR]

Medicine has become a necessary component of our daily life in the modern world. In this ever-changing environment, computer-aided diagnosis (CAD) has evolved as a dynamic and essential topic, providing crucial assistance to medical practitioners in their diagnostic endeavors. The main goal of this project is to create a CAD system that provides radiologists with a trustworthy second opinion during comprehensive detection of downscaled MIAS data. Our innovative approach is based on anisotropic denoising and the use of an alternating sequential filter (ASF) to identify potential mass sites. The method begins with ASF extraction through mathematical morphology, paving the way for overall contrast enhancement. To achieve this crucial aspect of our work, we use image-based active geometric contour models (level set) for spot segmentation. Our proposed strategy enables significant image improvements, leading to an increase in detection accuracy and efficient extraction of masses from the mini-MIAS mammography dataset. Experimental findings demonstrated that, when compared to contemporary methods, the proposed method produces satisfactory outcomes. The proposed technique achieved a sensitivity of 93.2%, an accuracy of 97.09%, a Dice coefficient (F1-score) exceeding 95.32%, and a specificity of 97.42%. [ABSTRACT FROM AUTHOR]

Estimating progression of acute ischemic brain lesions – or biological lesion age - holds huge practical importance for hyperacute stroke management. The current best method for determining lesion age from non-contrast computerised tomography (NCCT), measures Relative Intensity (RI), termed Net Water Uptake (NWU). We optimised lesion age estimation from NCCT using a convolutional neural network – radiomics (CNN-R) model trained upon chronometric lesion age (Onset Time to Scan: OTS), while validating against chronometric and biological lesion age in external datasets (N = 1945). Coefficients of determination (R2) for OTS prediction, using CNN-R, and RI models were 0.58 and 0.32 respectively; while CNN-R estimated OTS showed stronger associations with ischemic core:penumbra ratio, than RI and chronometric, OTS (ρ2 = 0.37, 0.19, 0.11); and with early lesion expansion (regression coefficients >2x for CNN-R versus others) (all comparisons: p < 0.05). Concluding, deep-learning analytics of NCCT lesions is approximately twice as accurate as NWU for estimating chronometric and biological lesion ages. [ABSTRACT FROM AUTHOR]

Predicting labels of gigapixel whole-slide images (WSIs) and localizing regions of interest (ROIs) with high precision are of great interest in computational pathology. The existing methods are mainly based on multi-instance learning (MIL) approach and its variants. However, such algorithms primarily treat the instances of slides as independent samples, which cannot effectively describe the relationship between instances in WSI. This would cause only a subset of high-score instances can be located, which is not applicable in clinical scenarios. Context-aware attention fusion adaptive hypergraph convolution network (CAF-AHGCN) is proposed to adaptively establish the local and global topology of cropped image patches spatially arranged in the slides. The framework combines hypergraph embedding representation with attention-based MIL pooling aggregation in a hierarchical feature fusion manner, which fully preserves high-order spatial structural correlations of the patches to make a slide-level prediction. Here, an adaptive hypergraph convolutional network is designed to dynamically adjust the correlation strength between the hypergraph nodes as the model goes deeper. Poly-1 loss and residual connection are also applied to prevent over-smoothing and improve the generalization ability of deep CAF-AHGCN model. We verified the superiority of CAF-AHGCN on two datasets, CAMELYON16 and TCGA-NSCLC. The results showed that the ACC, AUC and F1 score predicted by our model outperform other state-of-the-art algorithms on both datasets. The heat maps obtained by CAF-AHGCN are highly consistent with the pixel-wise annotated label of the WSI. The results show that CAF-AHGCN not only achieves high-accuracy label prediction for WSI, but also provides patch-wise human-interpretable features in ROI localization heatmaps. The outstanding performance of CAF-AHGCN framework provides a new perspective for future clinical applications of computer-aided diagnosis and intelligent systems. [ABSTRACT FROM AUTHOR]

Breast cancer is the most common cancer in women. Ultrasound is one of the most used techniques for diagnosis, but an expert in the field is necessary to interpret the test. Computer-aided diagnosis (CAD) systems aim to help physicians during this process. Experts use the Breast Imaging-Reporting and Data System (BI-RADS) to describe tumors according to several features (shape, margin, orientation...) and estimate their malignancy, with a common language. To aid in tumor diagnosis with BI-RADS explanations, this paper presents a deep neural network for tumor detection, description, and classification. An expert radiologist described with BI-RADS terms 749 nodules taken from public datasets. The YOLO detection algorithm is used to obtain Regions of Interest (ROIs), and then a model, based on a multi-class classification architecture, receives as input each ROI and outputs the BI-RADS descriptors, the BI-RADS classification (with 6 categories), and a Boolean classification of malignancy. Six hundred of the nodules were used for 10-fold cross-validation (CV) and 149 for testing. The accuracy of this model was compared with state-of-the-art CNNs for the same task. This model outperforms plain classifiers in the agreement with the expert (Cohen's kappa), with a mean over the descriptors of 0.58 in CV and 0.64 in testing, while the second best model yielded kappas of 0.55 and 0.59, respectively. Adding YOLO to the model significantly enhances the performance (0.16 in CV and 0.09 in testing). More importantly, training the model with BI-RADS descriptors enables the explainability of the Boolean malignancy classification without reducing accuracy. [ABSTRACT FROM AUTHOR]

Fibroadenoma is a common benign breast disease that affects women of all ages. Early diagnosis can greatly improve the treatment outcomes and reduce the associated pain. Computer-aided diagnosis (CAD) has great potential to improve diagnosis accuracy and efficiency. However, its application in sonography is limited. A network that utilizes expansive receptive fields and local information learning was proposed for the accurate segmentation of breast fibroadenomas in sonography. The architecture comprises the Hierarchical Attentive Fusion module, which conducts local information learning through channel-wise and pixel-wise perspectives, and the Residual Large-Kernel module, which utilizes multiscale large kernel convolution for global information learning. Additionally, multiscale feature fusion in both modules was included to enhance the stability of our network. Finally, an energy function and a data augmentation method were incorporated to fine-tune low-level features of medical images and improve data enhancement. The performance of our model is evaluated using both our local clinical dataset and a public dataset. Mean pixel accuracy (MPA) of 93.93% and 86.06% and mean intersection over union (MIOU) of 88.16% and 73.19% were achieved on the clinical and public datasets, respectively. They are significantly improved over state-of-the-art methods such as SegFormer (89.75% and 78.45% in MPA and 83.26% and 71.85% in MIOU, respectively). The proposed feature extraction strategy, combining local pixel-wise learning with an expansive receptive field for global information perception, demonstrates excellent feature learning capabilities. Due to this powerful and unique local-global feature extraction capability, our deep network achieves superior segmentation of breast fibroadenoma in sonography, which may be valuable in early diagnosis. [ABSTRACT FROM AUTHOR]

Abdominal organs play a significant role in regulating various functional systems. Any impairment in its functioning can lead to cancerous diseases. Diagnosing these diseases mainly relies on radiologists' subjective assessment, which varies according to professional abilities and clinical experience. Computer-Aided Diagnosis (CAD) system is designed to assist clinicians in identifying various pathological changes. Hence, automatic pancreas segmentation is a vital input to the CAD system in the diagnosis of cancer at its early stages. Automatic segmentation is achieved through traditional methods like atlas-based and statistical models, and nowadays, it is achieved through artificial intelligence approaches like machine learning and deep learning using various imaging modalities. This study investigates and analyses the various state-of-the-art multi-organ and pancreas segmentation approaches to identify the research gaps and future perspectives for the research community. The objective is achieved by framing the research questions using the PICOC framework and then selecting 140 research articles using a systematic process through the Covidence tool to conclude the answers to the respective questions. The literature search has been conducted on five databases of original studies published from 2003 to 2023. Initially, the literature analysis is presented in terms of publication, and the comparative analysis of the current study is presented with existing review studies. Then, existing studies are analyzed, focusing on semi-automatic and automatic multi-organ segmentation and pancreas segmentation, using various learning methods. Finally, the various critical issues, the research gaps and the future perspectives of segmentation methods based on published evidence are summarized. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is a progressive neurological disorder that gradually worsens over time, making early diagnosis difficult. Traditionally, diagnosis relies on a neurologist's detailed assessment of the patient's medical history and multiple scans. Recently, artificial intelligence (AI)-based computer-aided diagnosis (CAD) systems have demonstrated superior performance by capturing complex, nonlinear patterns in clinical data. However, the opaque nature of many AI models, often referred to as "black box" systems, has raised concerns about their transparency, resulting in hesitation among clinicians to trust their outputs. To address this challenge, we propose an explainable ensemble machine learning framework, XEMLPD, designed to provide both global and local interpretability in PD diagnosis while maintaining high predictive accuracy. Our study utilized two clinical datasets, carefully curated and optimized through a two-step data preprocessing technique that handled outliers and ensured data balance, thereby reducing bias. Several ensemble machine learning (EML) models—boosting, bagging, stacking, and voting—were evaluated, with optimized features selected using techniques such as SelectedKBest, mRMR, PCA, and LDA. Among these, the stacking model combined with LDA feature optimization consistently delivered the highest accuracy. To ensure transparency, we integrated explainable AI methods—SHapley Adaptive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME)—into the stacking model. These methods were applied post-evaluation, ensuring that each prediction is accompanied by a detailed explanation. By offering both global and local interpretability, the XEMLPD framework provides clear insights into the decision-making process of the model. This transparency aids clinicians in developing better treatment strategies and enhances the overall prognosis for PD patients. Additionally, our framework serves as a valuable tool for clinical data scientists in creating more reliable and interpretable CAD systems. [ABSTRACT FROM AUTHOR]

Cancer screening and diagnosis with the utilization of innovative Artificial Intelligence tools improved the treatment strategies and patients' survival. With the rapid development of imaging technologies and the rise of artificial intelligence (AI), there is a significant opportunity to improve cancer diagnostics through the combination of image analysis and AI algorithms. This article provides a comprehensive review of studies that have investigated the application of AI-assisted image processing in cancer diagnosis. We searched the Web of Science and Scopus databases to identify relevant studies published between 2014 and January 2024. The search strategy utilized targeted keywords such as cancer diagnostics, image analysis, artificial intelligence, and advanced imaging techniques. We limited the review to articles written in English and using AI-assisted image processing in cancer diagnosis. The results show that by leveraging machine learning algorithms, including deep learning, computer-aided diagnosis systems have been developed that are efficient in detecting tumors, thereby facilitating early cancer detection. Additionally, various authors have explored the integration of personalized treatment approaches and precision medicine, allowing for the development of treatment plans tailored to individual patient characteristics and needs. The review emphasizes the potential of AI-assisted image processing in revolutionizing cancer diagnostics. The insights gained from this study contribute to the current understanding of the field and pave the way for future research and development aimed at advancing cancer diagnostics using image analysis and artificial intelligence. [ABSTRACT FROM AUTHOR]

In this study, we developed a lightweight and rapid convolutional neural network (CNN) architecture for chest X-ray images; it primarily consists of a redesigned feature extraction (FE) module and multiscale feature (MF) module and validated using publicly available COVID-19 datasets. Experiments were conducted on multiple updated versions of the COVID-19 Radiography Database, a publicly accessible dataset on Kaggle. The database contained images categorized into three classes: COVID-19 coronavirus, viral or bacterial pneumonia, and normal. The results revealed that the proposed method achieved a training accuracy of 99.85% and a validation accuracy of 96.28% when detecting the three classes. In the test set, the optimal results were 96.03% accuracy for COVID-19, 97.10% accuracy for viral or bacterial pneumonia, and 97.86% accuracy for normal individuals. By reducing the computational requirements and improving the speed of the model, the proposed method can achieve real-time, low-error performance to help medical professionals with rapid diagnosis of COVID-19. [ABSTRACT FROM AUTHOR]

Breast cancer (BC) is a widespread and lethal cancer affecting women world- wide. Early diagnosis plays a pivotal role in ensuring survival, as late detection can result in a fatal outcome. Convolutional neural networks (CNNs) have made significant contributions to the task of medical imaging modalities and have dis- played promise in addressing this challenge. Recently, the success of the vision transformer (ViT) architecture has encouraged the use of the attention mecha- nism in computer-aided diagnosis (CAD) tasks. However, the ViT is known for its data-intensive nature and a substantial number of parameters and needs power- ful computer resources when training, which often leads to the same performance compared to CNNs. These challenges are particularly evident in tasks involving medical image datasets with complex images and limited data. This problem- atic situation led to the suggestion three of low-weight parameter systems based on convolution and attention techniques: vision transformer base model (ViT), compact convolution transformers (CCT), and lightweight mobile vision trans- formers (MVIT). These systems are developed by using the BreakHis dataset, which includes images captured at different magnification levels (40x, 100x, 200x, 400x), for both binary and multi classification of breast cancer subtypes. These low-weight hybrid ViT-CNN networks operate directly on input patches and convolution layers, to improve feature extraction and attention layers to train patches in all networks. This approach results in lower training time and fewer parameters while achieving accurate breast tumors classification. The proposed method is based on splitting the input image into patches and then focusing them on the area of cancerous lumps, providing a sequence of linear embedding of these patches as input. Second, we applied a convolution layer directly to the histopathology input patches, with the fewest possible modifications. Finally, we train patches in all transformer encoder layers to evaluate the performance of the classification of breast subtypes. The performance accuracies of our suggested models are 98.64% for VIT, 96.99% for CCT and 97.52% for MVIT. Moreover, the proposed models were compared with state-of-the-art models using the same dataset. Our study demonstrates how convolution and attention mechanisms can minimize computational training resources and decision time, to develop high- performing computer-aided analyses for breast cancer diagnosis. The source codes are accessible at https://github.com/abimouloud/ViT-CNN. [ABSTRACT FROM AUTHOR]

Knee Osteoarthritis (KOA), the most prevalent joint disease, significantly impacts elderly mobility due to progressive cartilage degeneration. Early prediction is crucial for preventing disease progression and guiding effective treatment plans. This paper proposes an EnsembleTL-ACO, fully automated, computer-aided diagnosis (CAD) system for accurate and rapid KOA severity grading. The proposed CAD system leverages an ensemble transfer learning strategy to extract robust deep features by fusing multiple deep learning models. It combines features from two consecutive AI models: (1) AlexNet for implicit class-wise deep feature extraction from preprocessed data, and (2) a custom IsrNet for further feature depth. Unsupervised k-means clustering based on PCA dimensionality reduction decomposes each class into subgroups, further refining features. Finally, Ant Colony Optimization (ACO) selects the most informative features. Evaluated on the Osteoarthritis Initiative (OAI) dataset, the proposed system achieves high accuracy in classifying the five KOA severity grades. With 1000 optimized features, it reaches average overall accuracies of 89.89% and 85.44% using Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) classifiers, respectively. This surpasses recent deep learning methods, demonstrating significant improvement. This novel CAD system presents a promising solution for practical applications, offering an accurate AI-powered tool for KOA diagnosis and management. [ABSTRACT FROM AUTHOR]

Viruses are submicroscopic agents that can infect other lifeforms and use their hosts' cells to replicate themselves. Despite having simplistic genetic structures among all living beings, viruses are highly adaptable, resilient, and capable of causing severe complications in their hosts' bodies. Due to their multiple transmission pathways, high contagion rate, and lethality, viruses pose the biggest biological threat both animal and plant species face. It is often challenging to promptly detect a virus in a host and accurately determine its type using manual examination techniques. However, computer-based automatic diagnosis methods, especially the ones using Transmission Electron Microscopy (TEM) images, have proven effective in instant virus identification. Using TEM images collected from a recent dataset, this article proposes a deep learning-based classification model to identify the virus type within those images. The methodology of this study includes two coherent image processing techniques to reduce the noise present in raw microscopy images and a functional Convolutional Neural Network (CNN) model for classification. Experimental results show that it can differentiate among 14 types of viruses with a maximum of 97.44% classification accuracy and F1-score, which asserts the effectiveness and reliability of the proposed method. Implementing this scheme will impart a fast and dependable virus identification scheme subsidiary to the thorough diagnostic procedures. [ABSTRACT FROM AUTHOR]

The automatic retinal screening system (ARSS) is a valuable computer-aided diagnosis tool for healthcare providers and public health initiatives. The ARSS facilitates mass retinal screenings that analyse retinal images and detect early signs of vision-threatening retinal diseases. The degradation in retinal image's naturalness causes imprecise diagnosis. This paper proposed a quality assessment method that is suitable for ARSS and is important for closing care gaps and reducing healthcare costs in the field of healthcare. A no-reference (NR) quality assessment method utilizing natural scene statistics (NSS) and the multi-resolution approach is developed to detect retinal image quality. Image quality classification is performed combining NSS features and statistical featurenns of retinal image. A support vector machine classifier is used to map the retinal image features and find image quality. The proposed method is compared with existing NR image quality assessment methods. The results show that the proposed method has improved accuracy, recall, precision and F-measure values of 3.42%, 3.66%, 1.63% and 2.66%, respectively, over the competing methods, demonstrating its suitability for ARSS. [ABSTRACT FROM AUTHOR]

Automated segmentation of liver tumors on CT scans is essential for aiding diagnosis and assessing treatment. Computer-aided diagnosis can reduce the costs and errors associated with manual processes and ensure the provision of accurate and reliable clinical assessments. However, liver tumors in CT images vary significantly in size and have fuzzy boundaries, making it difficult for existing methods to achieve accurate segmentation. Therefore, this paper proposes MAEG-Net, a multi-scale adaptive feature fusion liver tumor segmentation network based on edge guidance. Specifically, we design a multi-scale adaptive feature fusion module that effectively incorporates multi-scale information to better guide the segmentation of tumors of different sizes. Additionally, to address the problem of blurred tumor boundaries in images, we introduce an edge-aware guidance module to improve the model's feature learning ability under these conditions. Evaluation results on the liver tumor dataset (LiTS2017) show that our method achieves a Dice coefficient of 71.84% and a VOE of 38.64%, demonstrating the best performance for liver tumor segmentation in CT images. [ABSTRACT FROM AUTHOR]

Computer-assisted diagnosis (CAD) plays a key role in cancer diagnosis or screening. Whereas, current CAD performs poorly on whole slide image (WSI) analysis, and thus fails to generalize well. This research aims to develop an automatic classification system to distinguish between different types of carcinomas. Obtaining rich deep features in multi-class classification while achieving high accuracy is still a challenging problem. The detection and classification of cancerous cells in WSI are quite challenging due to the misclassification of normal lumps and cancerous cells. This is due to cluttering, occlusion, and irregular cell distribution. Researchers in the past mostly obtained the hand-crafted features while neglecting the above-mentioned challenges which led to a reduction of the classification accuracy. To mitigate this problem we proposed an efficient dual attention-based network (CytoNet). The proposed network is composed of two main modules (i) Efficient-Net and (ii) Dual Attention Module (DAM). Efficient-Net is capable of obtaining higher accuracy and enhancing efficiency as compared to existing Convolutional Neural Networks (CNNs). It is also useful to obtain the most generic features as it has been trained on ImageNet. Whereas DAM is very robust in obtaining attention and targeted features while negating the background. In this way, the combination of an efficient and attention module is useful to obtain the robust, and intrinsic features to obtain comparable performance. Further, we evaluated the proposed network on two well-known datasets (i) Our generated thyroid dataset (ii) Mendeley Cervical dataset (Hussain in Data Brief, 2019) with enhanced performance compared to their counterparts. CytoNet demonstrated a 99% accuracy rate on the thyroid dataset in comparison to its counterpart. The precision, recall, and F1-score values achieved on the Mendeley Cervical dataset are 0.992, 0.985, and 0.977, respectively. The code implementation is available on GitHub. https://github.com/naveedilyas/CytoNet-An-Efficient-Dual-Attention-based-Automatic-Prediction-of-Cancer-Sub-types-in-Cytol. [ABSTRACT FROM AUTHOR]

Glaucoma is one of the leading causes of visual impairment worldwide. If diagnosed too late, the disease can irreversibly cause severe damage to the optic nerve, resulting in permanent loss of central vision and blindness. Therefore, early diagnosis of the disease is critical. Recent advancements in machine learning techniques have greatly aided ophthalmologists in timely and efficient diagnosis through the use of automated systems. Training the machine learning models with the most informative features can significantly enhance their performance. However, selecting the most informative feature subset is a real challenge because there are 2n potential feature subsets for a dataset with n features, and the conventional feature selection techniques are also not very efficient. Thus, extracting relevant features from medical images and selecting the most informative is a challenging task. Additionally, a considerable field of study has evolved around the discovery and selection of highly influential features (characteristics) from a large number of features. Through the inclusion of the most informative features, this method has the potential to improve machine learning classifiers by enhancing their classification performance, reducing training and testing time, and lowering system diagnostic costs by incorporating the most informative features. This work aims in the same direction to propose a unique, novel, and highly efficient feature selection (FS) approach using the Whale Optimization Algorithm (WOA), the Grey Wolf Optimization Algorithm (GWO), and a hybridized version of these two metaheuristics. To the best of our knowledge, the use of these two algorithms and their amalgamated version for FS in human disease prediction, particularly glaucoma prediction, has been rare in the past. The objective is to create a highly influential subset of characteristics using this approach. The suggested FS strategy seeks to maximize classification accuracy while reducing the total number of characteristics used. We evaluated the efficacy of the proposed approach in classifying eye-related glaucoma illnesses. In this study, we aim to assist professionals in identifying glaucoma by utilizing a proposed clinical decision support system that integrates image processing, soft-computing algorithms, and machine learning, and validates it on benchmark fundus images. Initially, we extract 65 features from the 646 retinal fundus images in the ORIGA benchmark dataset, from which a subset of features is created. For two-class classification, different machine learning classifiers receive the elected features. Employing 5-fold and 10-fold stratified cross-validation has enhanced the generalized performance of the proposed model. We assess performance using several well-established statistical criteria. The tests show that the suggested computer-aided diagnosis (CAD) model has an F1-score of 97.50%, an accuracy score of 96.50%, a precision score of 97%, a sensitivity score of 98.10%, a specificity score of 93.30%, and an AUC score of 94.2% on the ORIGA dataset. To demonstrate its excellence, we compared the suggested approach's performance with other current state-of-the-art models. The suggested approach shows promising results in predicting glaucoma, potentially aiding in the early diagnosis and treatment of the disease. Furthermore, real-time applications showcase the proposed approach's suitability, enabling its deployment in areas lacking expert medical practitioners. Overburdened expert ophthalmologists can use this approach as a second opinion, as it requires very little time for processing the retinal fundus images. The proposed model can also aid, after incorporating required modifications, in making clinical decisions for various diseases like lung infection and, diabetic retinopathy. [ABSTRACT FROM AUTHOR]

Generalizability of AI colonoscopy algorithms is important for wider adoption in clinical practice. However, current techniques for evaluating performance on unseen data require expensive and time-intensive labels. We show that a "Masked Siamese Network" (MSN), trained to predict masked out regions of polyp images without labels, can predict the performance of Computer Aided Detection (CADe) of polyps on colonoscopies, without labels. This holds on Japanese colonoscopies even when MSN is only trained on Israeli colonoscopies, which differ in scoping hardware, endoscope software, screening guidelines, bowel preparation, patient demographics, and the use of techniques such as narrow-band imaging (NBI) and chromoendoscopy (CE). Since our technique uses neither colonoscopy-specific information nor labels, it has the potential to apply to more medical imaging domains. [ABSTRACT FROM AUTHOR]

The editorial discusses the effectiveness of artificial intelligence (AI) in improving colonoscopy quality for colorectal cancer (CRC) screening. AI, through computer-aided detection (CADe) and characterization (CADx), has shown promise in increasing adenoma detection rates (ADR) and polyp detection rates (PDR). However, there are limitations to AI implementation, such as the need for extensive training data and potential increased procedure time. While some studies have shown significant improvements in colonoscopy markers with AI, others have reported mixed results, indicating the need for further research and consideration of confounding factors before widespread adoption of AI in healthcare. [Extracted from the article]

Psoriasis, being a chronic, inflammatory, lifelong skin disorder, has become a major threat to the human population. The precise and effective diagnosis of psoriasis continues to be difficult for clinicians due to its varied nature. In northern India, the prevalence of psoriasis among adult population ranges from 0.44 to 2.8%. Chronic plaque psoriasis accounts for over 90% of cases. This study utilized a dataset of 325 raw images collected from a reputable local hospital using a digital camera under uniform lighting conditions. These images were processed to generate 496 image patches (both diseased and normal), which were then normalized and resized for model training. An automated psoriasis image recognition framework was developed using four state-of-the-art deep transfer learning models: VGG16, VGG19, MobileNetV1, and ResNet-50. The convolutional layers adopted various edge, shape, and color filters to generate the feature map for psoriasis detection. Each pre-trained model was adapted with two dense layers, one dropout layer, and one output layer to classify input images. Among these models, MobileNetV1 achieved the best performance, with 94.84% sensitivity, 89.37% specificity, and 97.24% overall accuracy. Hyper-parameter tuning was performed using grid search to optimize learning rates, batch sizes, and dropout rates. The AdaGrad (Adaptive gradient)) optimizer was chosen for its adaptive learning rate capabilities, facilitating quicker convergence in model performance. Consequently, the methodology's performance improved to 94.25% sensitivity, 96.42% specificity, and 99.13% overall accuracy. The model's performance was also compared with non-machine learning-based diagnostic methods, yielding a Dice coefficient of 0.98. However, the model's effectiveness is dependent upon high-quality input images, as poor image conditions may affect accuracy, and it may not generalize well across diverse demographics or psoriasis variations, highlighting the need for varied training datasets for robustness. [ABSTRACT FROM AUTHOR]

The early and accurate diagnosis of brain tumors is critical for effective treatment planning, with Magnetic Resonance Imaging (MRI) serving as a key tool in the non-invasive examination of such conditions. Despite the advancements in Computer-Aided Diagnosis (CADx) systems powered by deep learning, the challenge of accurately classifying brain tumors from MRI scans persists due to the high variability of tumor appearances and the subtlety of early-stage manifestations. This work introduces a novel adaptation of the EfficientNetv2 architecture, enhanced with Global Attention Mechanism (GAM) and Efficient Channel Attention (ECA), aimed at overcoming these hurdles. This enhancement not only amplifies the model's ability to focus on salient features within complex MRI images but also significantly improves the classification accuracy of brain tumors. Our approach distinguishes itself by meticulously integrating attention mechanisms that systematically enhance feature extraction, thereby achieving superior performance in detecting a broad spectrum of brain tumors. Demonstrated through extensive experiments on a large public dataset, our model achieves an exceptional high-test accuracy of 99.76%, setting a new benchmark in MRI-based brain tumor classification. Moreover, the incorporation of Grad-CAM visualization techniques sheds light on the model's decision-making process, offering transparent and interpretable insights that are invaluable for clinical assessment. By addressing the limitations inherent in previous models, this study not only advances the field of medical imaging analysis but also highlights the pivotal role of attention mechanisms in enhancing the interpretability and accuracy of deep learning models for brain tumor diagnosis. This research sets the stage for advanced CADx systems, enhancing patient care and treatment outcomes. [ABSTRACT FROM AUTHOR]

Blindness is preventable by early detection of ocular abnormalities. Computer-aided diagnosis for ocular abnormalities is built by analyzing retinal imaging modalities, for instance, Color Fundus Photography (CFP). This research aims to propose a multi-label detection of 28 ocular abnormalities consisting of frequent and rare abnormalities from a single CFP by using transformer-based semantic dictionary learning. Rare labels are usually ignored because of a lack of features. We tackle this condition by adding the co-occurrence dependency factor to the model from the linguistic features of the labels. The model learns the relation between spatial features and linguistic features represented as a semantic dictionary. The proposed method treats the semantic dictionary as one of the main important parts of the model. It acts as the query while the spatial features are the key and value. The experiments are conducted on the RFMiD dataset. The results show that the proposed method achieves the top 30% in Evaluation Set on the RFMiD dataset challenge. It also shows that treating the semantic dictionary as one of the strong factors in model detection increases the performance when compared with the method that treats the semantic dictionary as a weak factor. [ABSTRACT FROM AUTHOR]

Objectives: To identify lymphatic vascular space invasion (LVSI) and lymphatic node metastasis (LNM) status of endometrial cancer (EC) patients, using radiomics based on MRI images. Methods: Five hundred and ninety-eight EC patients between January 2015 and September 2020 from two institutions were retrospectively included. Tumoral regions on DWI, T1CE, and T2W images were manually outlined. Radiomics features were extracted from tumor region and peri-tumor region of different thicknesses. We established sub-models to select features from each smaller category. Using this method, we separately constructed radiomic signatures for intra-tumoral and peri-tumoral images using different sequences. We constructed intra-tumoral and peri-tumoral models by combining their features, and a multi-sequence model by combining logits. Models were trained with 397 patients and validated with 170 internal and 31 external patients. Results: For LVSI positive/LNM positive status identification, the multi-parameter MRI radiomics model achieved the area under curve (AUC) values of 0.771 (95%CI: [0.692–0.849])/0.801 (95%CI: [0.704, 0.898]) and 0.864 (95%CI: [0.728–1.000])/0.976 (95%CI: [0.919, 1.000]) in internal and external test cohorts, respectively. Conclusions: Intra-tumoral and peri-tumoral radiomics signatures based on mpMRI can both be used to identify LVSI or LNM status in EC patients non-invasively. Further studies on LVSI and LNM should pay attention to both of them. [ABSTRACT FROM AUTHOR]

Corneal transplantation is the primary treatment for irreversible corneal diseases, but due to limited donor availability, bioengineered corneal equivalents are being developed as a solution, with biocompatibility, structural integrity, and physical function considered key factors. Since conventional evaluation methods may not fully capture the complex properties of the cornea, there is a need for advanced imaging and assessment techniques. In this study, we proposed a deep learning-based automatic segmentation method for transplanted bioengineered corneal equivalents using optical coherence tomography to achieve a highly accurate evaluation of graft integrity and biocompatibility. Our method provides quantitative individual thickness values, detailed maps, and volume measurements of the bioengineered corneal equivalents, and has been validated through 14 days of monitoring. Based on the results, it is expected to have high clinical utility as a quantitative assessment method for human keratoplasties, including automatic opacity area segmentation and implanted graft part extraction, beyond animal studies. [ABSTRACT FROM AUTHOR]

Purpose: Parkinson disease (PD) is a common progressive neurodegenerative disorder in our ageing society. Early-stage PD biomarkers are desired for timely clinical intervention and understanding of pathophysiology. Since one of the characteristics of PD is the progressive loss of dopaminergic neurons in the substantia nigra pars compacta, we propose a feature extraction method for analysing the differences in the substantia nigra between PD and non-PD patients. Method: We propose a feature-extraction method for volumetric images based on a rank-1 tensor decomposition. Furthermore, we apply a feature selection method that excludes common features between PD and non-PD. We collect neuromelanin images of 263 patients: 124 PD and 139 non-PD patients and divide them into training and testing datasets for experiments. We then experimentally evaluate the classification accuracy of the substantia nigra between PD and non-PD patients using the proposed feature extraction method and linear discriminant analysis. Results: The proposed method achieves a sensitivity of 0.72 and a specificity of 0.64 for our testing dataset of 66 non-PD and 42 PD patients. Furthermore, we visualise the important patterns in the substantia nigra by a linear combination of rank-1 tensors with selected features. The visualised patterns include the ventrolateral tier, where the severe loss of neurons can be observed in PD. Conclusions: We develop a new feature-extraction method for the analysis of the substantia nigra towards PD diagnosis. In the experiments, even though the classification accuracy with the proposed feature extraction method and linear discriminant analysis is lower than that of expert physicians, the results suggest the potential of tensorial feature extraction. [ABSTRACT FROM AUTHOR]

Efficient and reliable storage, analysis, and transmission of medical images are imperative for accurate diagnosis, treatment, and management of various diseases. Since quantum computing can revolutionize big data analytics by providing faster solutions and security tactics, numerous studies in this field have focused on the use of quantum and quantum-inspired algorithms to enhance the performance of traditional medical image processing approaches. This review aims to provide readers with a succinct yet adequate compendium of the advances in medical image processing combined with quantum behaviors for disease diagnosis and medical image security. Some open challenges are outlined, identifying the performance limitations of current quantum technology in their applications, while addressing the short-, medium-, and long-term development plans of this field in designing future quantum healthcare systems. We hope that this review will provide full guidance for upcoming researchers interested in this area and will stimulate further appetite of experts already active in this area aimed at the pursuit of more advanced quantum paradigms in medical image processing applications. [ABSTRACT FROM AUTHOR]

Retinopathy of Prematurity (ROP) is a critical eye disorder affecting premature infants, characterized by abnormal blood vessel development in the retina. Plus Disease, indicating severe ROP progression, plays a pivotal role in diagnosis. Recent advancements in Artificial Intelligence (AI) have shown parity with or surpass human experts in ROP detection, especially Plus Disease. However, the success of AI systems depends on high-quality datasets, emphasizing the need for collaboration and data sharing among researchers. To address this challenge, the paper introduces a new public dataset, FARFUM-RoP (Farabi and Ferdowsi University of Mashhad's ROP dataset), comprising 1533 ROP fundus images from 68 patients, annotated independently by five experienced childhood ophthalmologists as "Normal," "Pre-Plus," or "Plus." Ethical principles and consent were meticulously followed during data collection. The paper presents the dataset structure, patient details, and expert labels. [ABSTRACT FROM AUTHOR]

In recent years a significant demand to develop computer-assisted diagnostic tools to assess prostate cancer using whole slide images has been observed. In this study we develop and validate a machine learning system for cancer assessment, inclusive of detection of perineural invasion and measurement of cancer portion to meet clinical reporting needs. The system analyses the whole slide image in three consecutive stages: tissue detection, classification, and slide level analysis. The whole slide image is divided into smaller regions (patches). The tissue detection stage relies upon traditional machine learning to identify WSI patches containing tissue, which are then further assessed at the classification stage where deep learning algorithms are employed to detect and classify cancer tissue. At the slide level analysis stage, entire slide level information is generated by aggregating all the patch level information of the slide. A total of 2340 haematoxylin and eosin stained slides were used to train and validate the system. A medical team consisting of 11 board certified pathologists with prostatic pathology subspeciality competences working independently in 4 different medical centres performed the annotations. Pixel-level annotation based on an agreed set of 10 annotation terms, determined based on medical relevance and prevalence, was created by the team. The system achieved an accuracy of 99.53% in tissue detection, with sensitivity and specificity respectively of 99.78% and 99.12%. The system achieved an accuracy of 92.80% in classifying tissue terms, with sensitivity and specificity respectively 92.61% and 99.25%, when 5x magnification level was used. For 10x magnification, these values were respectively 91.04%, 90.49%, and 99.07%. For 20x magnification they were 84.71%, 83.95%, 90.13%. [ABSTRACT FROM AUTHOR]

Diabetes retinopathy (DR) is the primary cause of blindness worldwide. Computer-aided diagnosis methods for early detection of DR fundus images are time and effort-saving and have a low risk of a misdiagnosis compared with a manual diagnosis of DR by clinical experts and ophthalmologists. Recently, transfer learning strategies of pre-trained models have been increasingly employed in medical image detection as a deep learning technique, and they effectively speed up the training phase. However, the effect of image enhancement and hyper-parameter tuning in DR detection was not reported in the literature. Furthermore, conventional transfer learning strategies, while widely employed, have exhibited limitations in achieving high accuracies in this context. Here, we employed the publicly available APTOS dataset for training models. Three different image enhancement techniques, including contrast enhancement, color constancy, and contrast-limited adaptive histogram equalization (CLAHE), are used on DR and its severity detection. Bayesian optimization method was employed for hyper-parameter tuning. Three different pre-trained convolutional neural networks, namely AlexNet, GoogLeNet, and SqueezeNet, are investigated on the binary and multi-classification of DR fundus images. The AlexNet model with tuned training hyper-parameters showed an accuracy of 99.10% for multi-classification of DR. The modified AlexNet + genetic algorithm and tuned hyper-parameters model achieved an impressive accuracy of 99.81% in the same multi-level classification of DR severity. The CLAHE enhancement technique outperforms other techniques in these approaches. The reliable performance of the proposed method would enhance the rapid and robust detection of DR utilizing fundus images to intervene effectively before vision loss occurs. [ABSTRACT FROM AUTHOR]

Increasing interest in applying large language models (LLMs) to medicine is due in part to their impressive performance on medical exam questions. However, these exams do not capture the complexity of real patient–doctor interactions because of factors like patient compliance, experience, and cognitive bias. We hypothesized that LLMs would produce less accurate responses when faced with clinically biased questions as compared to unbiased ones. To test this, we developed the BiasMedQA dataset, which consists of 1273 USMLE questions modified to replicate common clinically relevant cognitive biases. We assessed six LLMs on BiasMedQA and found that GPT-4 stood out for its resilience to bias, in contrast to Llama 2 70B-chat and PMC Llama 13B, which showed large drops in performance. Additionally, we introduced three bias mitigation strategies, which improved but did not fully restore accuracy. Our findings highlight the need to improve LLMs' robustness to cognitive biases, in order to achieve more reliable applications of LLMs in healthcare. [ABSTRACT FROM AUTHOR]

Cervical cancer is the fourth most common malignant tumor among women globally, posing a significant threat to women's health. In 2022, approximately 600,000 new cases were reported, and 340,000 deaths occurred due to cervical cancer. Magnetic resonance imaging (MRI) is the preferred imaging method for diagnosing, staging, and evaluating cervical cancer. However, manual segmentation of MRI images is time-consuming and subjective. Therefore, there is an urgent need for automatic segmentation models to identify cervical cancer lesions in MRI scans accurately. All MRIs in our research are from cervical cancer patients diagnosed by pathology at Tongren City People's Hospital. Strict data selection criteria and clearly defined inclusion and exclusion conditions were established to ensure data consistency and accuracy of research results. The dataset contains imaging data from 122 cervical cancer patients, with each patient having 100 pelvic dynamic contrast-enhanced MRI scans. Annotations were jointly completed by medical professionals from Universiti Putra Malaysia and the Radiology Department of Tongren City People's Hospital to ensure data accuracy and reliability. Additionally, a novel computer-aided diagnosis model named SwinUNeCCt is proposed. This model incorporates (i) A bidirectional hash-based agent multi-head self-attention mechanism, which optimizes the interaction between local and global features in MRI, aiding in more accurate lesion identification. (ii) Reduced computational complexity of the self-attention mechanism. The effectiveness of the SwinUNeCCt model has been validated through comparisons with state-of-the-art 3D medical models, including nnUnet, TransBTS, nnFormer, UnetR, UnesT, SwinUNetR, and SwinUNeLCsT. In semantic segmentation tasks without a classification module, the SwinUNeCCt model demonstrates excellent performance across multiple key metrics: achieving a 95HD of 6.25, an IoU of 0.669, and a DSC of 0.802, all of which are the best results among the compared models. Simultaneously, SwinUNeCCt strikes a good balance between computational efficiency and model complexity, requiring only 442.7 GFLOPs of computational power and 71.2 M parameters. Furthermore, in semantic segmentation tasks that include a classification module, the SwinUNeCCt model also exhibits powerful recognition capabilities. Although this slightly increases computational overhead and model complexity, its performance surpasses other comparative models. The SwinUNeCCt model demonstrates excellent performance in semantic segmentation tasks, achieving the best results among state-of-the-art 3D medical models across multiple key metrics. It balances computational efficiency and model complexity well, maintaining high performance even with the inclusion of a classification module. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a global healthcare challenge lacking a simple and affordable detection method. We propose a novel deep learning framework, Eye-AD, to detect Early-onset Alzheimer's Disease (EOAD) and Mild Cognitive Impairment (MCI) using OCTA images of retinal microvasculature and choriocapillaris. Eye-AD employs a multilevel graph representation to analyze intra- and inter-instance relationships in retinal layers. Using 5751 OCTA images from 1671 participants in a multi-center study, our model demonstrated superior performance in EOAD (internal data: AUC = 0.9355, external data: AUC = 0.9007) and MCI detection (internal data: AUC = 0.8630, external data: AUC = 0.8037). Furthermore, we explored the associations between retinal structural biomarkers in OCTA images and EOAD/MCI, and the results align well with the conclusions drawn from our deep learning interpretability analysis. Our findings provide further evidence that retinal OCTA imaging, coupled with artificial intelligence, will serve as a rapid, noninvasive, and affordable dementia detection. [ABSTRACT FROM AUTHOR]

Abdominal aortic aneurysm (AAA) often remains undetected until rupture due to limited access to diagnostic ultrasound. This trial evaluated a deep learning (DL) algorithm to guide AAA screening by novice nurses with no prior ultrasonography experience. Ten nurses performed 15 scans each on patients over 65, assisted by a DL object detection algorithm, and compared against physician-performed scans. Ultrasound scan quality, assessed by three blinded expert physicians, was the primary outcome. Among 184 patients, DL-guided novices achieved adequate scan quality in 87.5% of cases, comparable to the 91.3% by physicians (p = 0.310). The DL model predicted AAA with an AUC of 0.975, 100% sensitivity, and 97.8% specificity, with a mean absolute error of 2.8 mm in predicting aortic width compared to physicians. This study demonstrates that DL-guided POCUS has the potential to democratize AAA screening, offering performance comparable to experienced physicians and improving early detection. [ABSTRACT FROM AUTHOR]

Computer-aided diagnosis (CAD) techniques, exemplified by chest x-ray (CXR)-based methods, offer a cost-effective alternative for early-stage COVID-19 diagnosis compared to expensive options such as polymerase chain reaction (PCR) and computed tomography (CT) scan. Despite efforts to diagnose COVID-19 with CXR-based methods, their performance could be improved by considering the spatial relationships between regions of interest (ROIs) in CXR images. This oversight hinders the ability to accurately identify areas of the human lung most vulnerable to COVID-19. This model implements a two-way classification system to differentiate between lung X-ray impressions, accurately determining whether they are affected or normal. The effectiveness of this system is assessed using metrics such as accuracy, recall, precision, and F1-score. We employed over 2409 samples of X-ray images in the COVID-19 diagnosis process. The results obtained from the VGG16 model showcase outstanding performance, with a recognition rate of 99.58% for X-ray images and 94.29% for CT-scan pictures within the given sample size and two-class categorization. This model surpasses all existing approaches documented in the literature. Medical professionals and healthcare workers can effectively utilize this proposed system, leveraging X-rays and CT scans of human lungs to identify COVID-19 cases accurately. [ABSTRACT FROM AUTHOR]