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Recent progress in Deep Learning (DL) has shown potential in intelligent healthcare applications, enhancing patients' quality of life. However, improving DL precision requires a larger and diverse dataset, leading to privacy and confidentiality challenges when consolidating data at a centralized server. To address this, we propose a skin cancer detection method prioritizing patient information and privacy. "Skin-net," a novel Convolutional Neural Network (CNN) model, integrates progressively private Federated Learning (FL) for accurate classification of complex skin lesion images. FL ensures data confidentiality during model training. Skin-net achieves promising results, with 98.3%± accuracy, 98.8%± sensitivity, and 97.9%± specificity, while preserving data privacy. It offers an effective pathway for skin cancer analysis and image augmentation, mitigating privacy concerns in medical image analysis. [ABSTRACT FROM AUTHOR]

Current machine learning methods for medical image analysis primarily focus on developing models tailored for their specific tasks, utilizing data within their target domain. These specialized models tend to be data-hungry and often exhibit limitations in generalizing to out-of-distribution samples. In this work, we show that employing models that incorporate multiple domains instead of specialized ones significantly alleviates the limitations observed in specialized models. We refer to this approach as multi-domain model and compare its performance to that of specialized models. For this, we introduce the incorporation of diverse medical image domains, including different imaging modalities like X-ray, MRI, CT, and ultrasound images, as well as various viewpoints such as axial, coronal, and sagittal views. Our findings underscore the superior generalization capabilities of multi-domain models, particularly in scenarios characterized by limited data availability and out-of-distribution, frequently encountered in healthcare applications. The integration of diverse data allows multi-domain models to utilize information across domains, enhancing the overall outcomes substantially. To illustrate, for organ recognition, multi-domain model can enhance accuracy by up to 8% compared to conventional specialized models. [ABSTRACT FROM AUTHOR]

With the rapid advancement of deep learning technologies, self-supervised learning utilizing large-scale unlabeled datasets has emerged as a dominant learning paradigm across multiple fields. This paradigm aligns well with the nature of medical imaging data, which has led to significant research efforts in applying self-supervised learning methods to this domain. However, many of these approaches fail to fully consider the unique characteristics of medical imaging, particularly the critical role that texture information plays in the diagnosis of thorax diseases. To address this gap, we propose a novel texture-aware self-supervised learning framework that leverages the Gray-Level Co-occurrence Matrix (GLCM) as an auxiliary signal to strengthen the model’s capacity to extract disease-relevant texture features. Additionally, we integrate curriculum learning into our approach, which gradually emphasizes texture information throughout the training process. This method enables the model to more effectively capture the inherent characteristics of medical imaging data. Our qualitative and quantitative experimental results show that our approach surpasses the current state-of-the-art methods on both the NIH CXR and Stanford CheXpert datasets. [ABSTRACT FROM AUTHOR]

In the rapidly evolving field of medical image analysis utilizing artificial intelligence (AI), the selection of appropriate computational models is critical for accurate diagnosis and patient care. This literature review provides a comprehensive comparison of vision transformers (ViTs) and convolutional neural networks (CNNs), the two leading techniques in the field of deep learning in medical imaging. We conducted a survey systematically. Particular attention was given to the robustness, computational efficiency, scalability, and accuracy of these models in handling complex medical datasets. The review incorporates findings from 36 studies and indicates a collective trend that transformer-based models, particularly ViTs, exhibit significant potential in diverse medical imaging tasks, showcasing superior performance when contrasted with conventional CNN models. Additionally, it is evident that pre-training is important for transformer applications. We expect this work to help researchers and practitioners select the most appropriate model for specific medical image analysis tasks, accounting for the current state of the art and future trends in the field. [ABSTRACT FROM AUTHOR]

Content-based image retrieval (CBIR) systems are vital for managing the large volumes of data produced by medical imaging technologies. They enable efficient retrieval of relevant medical images from extensive databases, supporting clinical diagnosis, treatment planning, and medical research.This study aims to enhance CBIR systems’ effectiveness in medical image analysis by introducing the VisualSift Ensembling Integration with Attention Mechanisms (VEIAM). VEIAM seeks to improve diagnostic accuracy and retrieval efficiency by integrating robust feature extraction with dynamic attention mechanisms.VEIAM combines Scale-Invariant Feature Transform (SIFT) with selective attention mechanisms to emphasize crucial regions within medical images dynamically. Implemented in Python, the model integrates seamlessly into existing medical image analysis workflows, providing a robust and accessible tool for clinicians and researchers.The proposed VEIAM model demonstrated an impressive accuracy of 97.34% in classifying and retrieving medical images. This performance indicates VEIAM’s capability to discern subtle patterns and textures critical for accurate diagnostics.By merging SIFT-based feature extraction with attention processes, VEIAM offers a discriminatively powerful approach to medical image analysis. Its high accuracy and efficiency in retrieving relevant medical images make it a promising tool for enhancing diagnostic processes and supporting medical research in CBIR systems. [ABSTRACT FROM AUTHOR]

Introduction: Early detection of pancreatic cancer continues to be a challenge due to the difficulty in accurately identifying specific signs or symptoms that might correlate with the onset of pancreatic cancer. Unlike breast or colon or prostate cancer where screening tests are often useful in identifying cancerous development, there are no tests to diagnose pancreatic cancers. As a result, most pancreatic cancers are diagnosed at an advanced stage, where treatment options, whether systemic therapy, radiation, or surgical interventions, offer limited efficacy. Methods: A two-stage weakly supervised deep learning-based model has been proposed to identify pancreatic tumors using computed tomography (CT) images from Henry Ford Health (HFH) and publicly available Memorial Sloan Kettering Cancer Center (MSKCC) data sets. In the first stage, the nnU-Net supervised segmentation model was used to crop an area in the location of the pancreas, which was trained on the MSKCC repository of 281 patient image sets with established pancreatic tumors. In the second stage, a multi-instance learning-based weakly supervised classification model was applied on the cropped pancreas region to segregate pancreatic tumors from normal appearing pancreas. The model was trained, tested, and validated on images obtained from an HFH repository with 463 cases and 2,882 controls. Results: The proposed deep learning model, the two-stage architecture, offers an accuracy of 0.907 ± 0.01, sensitivity of 0.905 ± 0.01, specificity of 0.908 ± 0.02, and AUC (ROC) 0.903 ± 0.01. The two-stage framework can automatically differentiate pancreatic tumor from non-tumor pancreas with improved accuracy on the HFH dataset. Discussion: The proposed two-stage deep learning architecture shows significantly enhanced performance for predicting the presence of a tumor in the pancreas using CT images compared with other reported studies in the literature. [ABSTRACT FROM AUTHOR]

Lung cancer is a leading cause of cancer-related deaths worldwide, emphasizing the significance of early detection. Computer-aided diagnostic systems have emerged as valuable tools for aiding radiologists in the analysis of medical images, particularly in the context of lung cancer screening. A typical pipeline for lung cancer diagnosis involves pulmonary nodule detection, segmentation, and classification. Although traditional machine learning methods have been deployed in the previous years with great success, this literature review focuses on state-of-the-art deep learning methods. The objective is to extract key insights and methodologies from deep learning studies that exhibit high experimental results in this domain. This paper delves into the databases utilized, preprocessing steps applied, data augmentation techniques employed, and proposed methods deployed in studies with exceptional outcomes. The reviewed studies predominantly harness cutting-edge deep learning methodologies, encompassing traditional convolutional neural networks (CNNs) and advanced variants such as 3D CNNs, alongside other innovative approaches such as Capsule networks and transformers. The methods examined in these studies reflect the continuous evolution of deep learning techniques for pulmonary nodule detection, segmentation, and classification. The methodologies, datasets, and techniques discussed here collectively contribute to the development of more efficient computer-aided diagnostic systems, empowering radiologists and dfhealthcare professionals in the fight against this deadly disease. [ABSTRACT FROM AUTHOR]

Background: AI medical image analysis shows potential applications in research on premature aging and skin. The purpose of this study was to explore the mechanism of the Zuogui pill based on artificial intelligence medical image analysis on ovarian function enhancement and skin elasticity repair in rats with premature aging. Materials and Methods: The premature aging rat model was established by using an experimental animal model. Then Zuogui pills were injected into the rats with premature aging, and the images were detected by an optical microscope. Then, through the analysis of artificial intelligence medical images, the image data is analyzed to evaluate the indicators of ovarian function. Results: Through optical microscope image detection, we observed that the Zuogui pill played an active role in repairing ovarian tissue structure and increasing the number of follicles in mice, and Zuogui pill also significantly increased the level of progesterone in the blood of mice. Conclusion: Most of the ZGP‐induced outcomes are significantly dose‐dependent. [ABSTRACT FROM AUTHOR]

Advancements in imaging technologies have revolutionized our ability to deeply profile pathological tissue architectures, generating large volumes of imaging data with unparalleled spatial resolution. This type of data collection, namely, spatial proteomics, offers invaluable insights into various human diseases. Simultaneously, computational algorithms have evolved to manage the increasing dimensionality of spatial proteomics inherent in this progress. Numerous imaging-based computational frameworks, such as computational pathology, have been proposed for research and clinical applications. However, the development of these fields demands diverse domain expertise, creating barriers to their integration and further application. This review seeks to bridge this divide by presenting a comprehensive guideline. We consolidate prevailing computational methods and outline a roadmap from image processing to data-driven, statistics-informed biomarker discovery. Additionally, we explore future perspectives as the field moves toward interfacing with other quantitative domains, holding significant promise for precision care in immuno-oncology. [ABSTRACT FROM AUTHOR]

Medical image analysis tasks are characterized by high-noise, volumetric, and multi-modality, posing challenges for the model that attempts to learn robust features from the input images. Over the last decade, deep neural networks (DNNs) have achieved enormous success in medical image analysis tasks, which can be attributed to their powerful feature representation capability. Despite the promising results reported in numerous literature, DNNs are also criticized for several pivotal limits, with one of the limitations is lack of safety. Safety plays an important role in the applications of DNNs during clinical practice, helping the model defend against potential attacks and preventing the model from silent failure prediction. The recently proposed neural ordinary differential equation (NODE), a continuous model bridging the gap between DNNs and ODE, provides a significant advantage in ensuring the model’s safety. Among the variants of NODE, the neural memory ordinary differential equation (nmODE) owns the global attractor theoretically, exhibiting superiority in prompting the model’s performance and robustness during applications. While NODE and its variants have been widely used in medical image analysis tasks, there is a lack of a comprehensive review of their applications, hindering the in-depth understanding of NODE’s working principle and its potential applications. To mitigate this limitation, this paper thoroughly reviews the literature on the applications of NODE in medical image analysis from the following five aspects: segmentation, reconstruction, registration, disease prediction, and data generation. We also summarize both the strengths and downsides of the applications of NODE, followed by the possible research directions. To the best of our knowledge, this is the first review regards the applications of NODE in the field of medical image analysis. We hope this review can draw the researchers’ attention to the great potential of NODE and its variants in medical image analysis. [ABSTRACT FROM AUTHOR]

Federated Learning is a decentralized machine learning method that allows collaborative model training across several devices or institutions while maintaining the privacy and localization of data. Since the raw data is used locally, this collaborative method enables the development of a strong and precise global model without jeopardizing the privacy and security of sensitive data. The healthcare sector is an important one that focuses on preserving and enhancing people's health through medical services, diagnoses, treatments, and preventative measures. Efficient evaluation of Federated Learning in the Internet of Medical Things (IoMT) enables breakthroughs in medical image analysis, electronic health record analysis, personalized treatment planning, and drug development by enabling institutions to train models locally on sensitive patient information without sharing raw data. This paper presents the role of Federated Learning in healthcare and current trends in Federated Learning-based healthcare. A case study is presented on deep Federated Learning for privacy-preserving in healthcare. Finally, challenges and future research directions are discussed in the paper. [ABSTRACT FROM AUTHOR]

Diabetic foot ulcers (DFUs) are a prevalent and serious complication of diabetes, often leading to severe morbidity and even amputations if not timely diagnosed and managed. The increasing prevalence of DFUs poses a significant challenge to healthcare systems worldwide. Accurate and timely classification of DFUs is crucial for effective treatment and prevention of complications. In this paper, we present "DFootNet", an innovative and comprehensive classification framework for the accurate assessment of diabetic foot ulcers using a dense neural network architecture. Our proposed approach leverages the power of deep learning to automatically extract relevant features from diverse clinical DFU images. The proposed model comprises a multi-layered dense neural network designed to handle the intricate patterns and variations present in different stages and types of DFUs. The network architecture integrates convolutional and fully connected layers, allowing for hierarchical feature extraction and robust feature representation. To evaluate the efficacy of DFootNet, we conducted experiments on a large and diverse dataset of diabetic foot ulcers. Our results demonstrate that DFootNet achieves a remarkable accuracy of 98.87%, precision—99.01%, recall—98.73%, F1-score as 98.86%, and AUC-ROC as 98.13%, outperforming existing methods in distinguishing between ulcer and non-ulcer images. Moreover, our framework provides insights into the decision-making process, offering transparency and interpretability through attention mechanisms that highlight important regions within ulcer images. We also present a comparative analysis of DFootNet's performance against other popular deep learning models, showcasing its robustness and adaptability across various scenarios. [ABSTRACT FROM AUTHOR]

This study explores the practical applications of artificial intelligence (AI) in medical imaging, focusing on machine learning classifiers and deep learning models. The aim is to improve detection processes and diagnose diseases effectively. The study emphasizes the importance of teamwork in harnessing AI's full potential for image analysis. Collaboration between doctors and AI experts is crucial for developing AI tools that bridge the gap between concepts and practical applications. The study demonstrates the effectiveness of machine learning classifiers, such as forest algorithms and deep learning models, in image analysis. These techniques enhance accuracy and expedite image analysis, aiding in the development of accurate medications. The study evidenced that technologically assisted medical image analysis significantly improves efficiency and accuracy across various imaging modalities, including X-ray, ultrasound, CT scans, MRI, etc. The outcomes were supported by the reduced diagnosis time. The exploration also helps us to understand the ethical considerations related to the privacy and security of data, bias, and fairness in algorithms, as well as the role of medical consultation in ensuring responsible AI use in healthcare. [ABSTRACT FROM AUTHOR]

Diabetic Retinopathy (DR) stands as one of the most prevalent complications among individuals with diabetes, potentially resulting in irreversible vision loss. Recent efforts within the research community have focused on developing Computer-Aided Diagnosis tools, harnessing color fundus retinal scan images to automate the assessment of diabetic retinopathy severity grades. Leveraging the latest advancements in Computer Vision and Neural Networks, these solutions have demonstrated impressive accuracy in identifying the early stages of retinopathy. However, their performance diminishes when faced with higher severity grades, likely due to the scarcity of labeled data for such cases. This study aims to address this limitation by delving into deep spatial representations derived from color fundus retinal scan images associated with higher severity grades. This is possibly due to the small number of labelled samples available for higher severity grades. Different from the existing approaches, we exploit deep spatial representations extracted from a diverse set of pre-trained deep convolutional neural networks to craft an Adaptive Ensemble Classifier. This novel methodology excels at accurately classifying the severity grades of diabetic retinopathy. Our experiments, conducted on the Kaggle APTOS-2019 benchmark dataset, illustrate the superiority of multi-modal deep spatial representations when utilized in conjunction with the Adaptive Ensemble Classifier, by achieving 81.86% accuracy and surpassing the performance of hand-crafted and uni-modal representations for retinal scan images. These findings offer a promising stride towards enhancing the accuracy of diabetic retinopathy diagnosis, particularly in the context of more advanced severity grades. [ABSTRACT FROM AUTHOR]

Lung and colon cancers are leading contributors to cancer-related fatalities globally, distinguished by unique histopathological traits discernible through medical imaging. Effective classification of these cancers is critical for accurate diagnosis and treatment. This study addresses critical challenges in the diagnostic imaging of lung and colon cancers, which are among the leading causes of cancer-related deaths worldwide. Recognizing the limitations of existing diagnostic methods, which often suffer from overfitting and poor generalizability, our research introduces a novel deep learning framework that synergistically combines the Xception and MobileNet architectures. This innovative ensemble model aims to enhance feature extraction, improve model robustness, and reduce overfitting. Our methodology involves training the hybrid model on a comprehensive dataset of histopathological images, followed by validation against a balanced test set. The results demonstrate an impressive classification accuracy of 99.44%, with perfect precision and recall in identifying certain cancerous and non-cancerous tissues, marking a significant improvement over traditional approach. The practical implications of these findings are profound. By integrating Gradient-weighted Class Activation Mapping (Grad-CAM), the model offers enhanced interpretability, allowing clinicians to visualize the diagnostic reasoning process. This transparency is vital for clinical acceptance and enables more personalized, accurate treatment planning. Our study not only pushes the boundaries of medical imaging technology but also sets the stage for future research aimed at expanding these techniques to other types of cancer diagnostics. [ABSTRACT FROM AUTHOR]

Pediatric respiratory disease diagnosis and subsequent treatment require accurate and interpretable analysis. A chest X-ray is the most cost-effective and rapid method for identifying and monitoring various thoracic diseases in children. Recent developments in self-supervised and transfer learning have shown their potential in medical imaging, including chest X-ray areas. In this article, we propose a three-stage framework with knowledge transfer from adult chest X-rays to aid the diagnosis and interpretation of pediatric thorax diseases. We conducted comprehensive experiments with different pre-training and fine-tuning strategies to develop transformer or convolutional neural network models and then evaluate them qualitatively and quantitatively. The ViT-Base/16 model, fine-tuned with the CheXpert dataset, a large chest X-ray dataset, emerged as the most effective, achieving a mean AUC of 0.761 (95% CI: 0.759–0.763) across six disease categories and demonstrating a high sensitivity (average 0.639) and specificity (average 0.683), which are indicative of its strong discriminative ability. The baseline models, ViT-Small/16 and ViT-Base/16, when directly trained on the Pediatric CXR dataset, only achieved mean AUC scores of 0.646 (95% CI: 0.641–0.651) and 0.654 (95% CI: 0.648–0.660), respectively. Qualitatively, our model excels in localizing diseased regions, outperforming models pre-trained on ImageNet and other fine-tuning approaches, thus providing superior explanations. The source code is available online and the data can be obtained from PhysioNet. [ABSTRACT FROM AUTHOR]

In healthcare, medical practitioners employ various imaging techniques such as CT, X-ray, PET, and MRI to diagnose patients, emphasizing the crucial need for early disease detection to enhance survival rates. Medical Image Analysis (MIA) has undergone a transformative shift with the integration of Artificial Intelligence (AI) techniques such as Machine Learning (ML) and Deep Learning (DL), promising advanced diagnostics and improved healthcare outcomes. Despite these advancements, a comprehensive understanding of the efficiency metrics, computational complexities, interpretability, and scalability of AI based approaches in MIA is essential for practical feasibility in real-world healthcare environments. Existing studies exploring AI applications in MIA lack a consolidated review covering the major MIA stages and specifically focused on evaluating the efficiency of AI based approaches. The absence of a structured framework limits decision-making for researchers, practitioners, and policymakers in selecting and implementing optimal AI approaches in healthcare. Furthermore, the lack of standardized evaluation metrics complicates methodology comparison, hindering the development of efficient approaches. This article addresses these challenges through a comprehensive review, taxonomy, and analysis of existing AI-based MIA approaches in healthcare. The taxonomy covers major image processing stages, classifying AI approaches for each stage based on method and further analyzing them based on image origin, objective, method, dataset, and evaluation metrics to reveal their strengths and weaknesses. Additionally, comparative analysis conducted to evaluate the efficiency of AI based MIA approaches over five publically available datasets: ISIC 2018, CVC-Clinic, 2018 DSB, DRIVE, and EM in terms of accuracy, precision, Recall, F-measure, mIoU, and specificity. The popular public datasets and evaluation metrics are briefly described and analyzed. The resulting taxonomy provides a structured framework for understanding the AI landscape in healthcare, facilitating evidence-based decision-making and guiding future research efforts toward the development of efficient and scalable AI approaches to meet current healthcare needs. [ABSTRACT FROM AUTHOR]

Brain tumours are a major public health concern, and early and accurate detection is critical in treatment. Early and precise detection of brain tumors is paramount, yet current technologies often struggle to achieve the necessary level of accuracy due to inherent limitations in image processing and classification methodologies. While approaches like convolutional neural networks and optimization techniques have shown promise, they often fall short in capturing intricate patterns and textures or achieving sufficient sensitivity, emphasizing the need for more advanced and integrated solutions like the proposed BioSwarmNet model. The system includes a meticulously designed image processing pipeline that ensures data consistency and quality. BioSwarmNet, a novel combination of Fractional Order Differential Particle Swarm Optimisation (FODPSO) and Recurrent Neural Networks (RNNs), uses swarm intelligence and deep learning to revolutionize medical image classification. Using the well-known BRATS dataset, this study provides a promising avenue for improving diagnostic accuracy and efficiency in brain tumour detection, which has the potential to benefit both healthcare professionals and patients. Notably, the proposed system outperformed in key metrics such as 99.12% accuracy, 98.62% sensitivity, and 99.86% specificity. [ABSTRACT FROM AUTHOR]

Abstract Current machine learning methods for medical image analysis primarily focus on developing models tailored for their specific tasks, utilizing data within their target domain. These specialized models tend to be data-hungry and often exhibit limitations in generalizing to out-of-distribution samples. In this work, we show that employing models that incorporate multiple domains instead of specialized ones significantly alleviates the limitations observed in specialized models. We refer to this approach as multi-domain model and compare its performance to that of specialized models. For this, we introduce the incorporation of diverse medical image domains, including different imaging modalities like X-ray, MRI, CT, and ultrasound images, as well as various viewpoints such as axial, coronal, and sagittal views. Our findings underscore the superior generalization capabilities of multi-domain models, particularly in scenarios characterized by limited data availability and out-of-distribution, frequently encountered in healthcare applications. The integration of diverse data allows multi-domain models to utilize information across domains, enhancing the overall outcomes substantially. To illustrate, for organ recognition, multi-domain model can enhance accuracy by up to 8% compared to conventional specialized models.

It has been established that 7.2% of patients hospitalized with coronavirus disease (COVID-19) exhibit signs of heart disease, with 23% of these patients experiencing heart failure. Currently, there is a lack of data on chest computed tomography (CT) for diagnosing myocarditis associated with COVID-19. The aim. To justify the feasibility and develop classification models for diagnosing myocarditis in COVID-19 patients based on chest CT data processing. Materials and methods. A retrospective analysis of data from 140 COVID-19 patients was conducted. Chest CT scans were analyzed using DRAGONFLY software, with permission from Object Research Systems. The COVID-CT-MD database, which includes CT data from 169 confirmed cases of SARS-CoV-2 infection, was used to build classification models. The regions of interest were fragments of heart CT images. Texture analysis methods were employed to create diagnostic models. Results. It was shown that the average density of the myocardium of a patient with a confirmed diagnosis of SARS-CoV-2 infection according to the Hounsfield scale does not essentially differ from the densitometric indicators of a healthy person. Therefore, the research was focused on finding structural changes in CT images for their use in constructing diagnostic models. The use of different classification algorithms had little effect on classification accuracy, probably due to the informational content of the input data. However, the obtained accuracy of the diagnostic models is acceptable and allows them to be used to support medical decision-making regarding diagnosis and treatment. Conclusions. Using classic methods, myocarditis was diagnosed in 7.1% of patients with severe pneumonia caused by the coronavirus. The global data closely aligns with the results of our clinical studies. The obtained results allowed for assessing structural changes in the myocardium characteristic of the acute form of SARS-CoV-2 infection. The constructed classification models indicate that specific changes in the myocardium during the acute form of SARS-CoV-2 infection can be identified using CT. The highest diagnostic accuracy on test samples reached 74%. The implementation of the developed diagnostic programs based on texture analysis of CT data and artificial intelligence technologies enables the diagnosis of myocarditis and the assessment of long-term treatment efficiency. Creation of these diagnostic programs using artificial intelligence technologies significantly simplifies the work of radiologists and improves the efficiency of myocarditis diagnosis in patients with SARS-CoV-2 infection.

This work aims to implement and utilize an advanced computer system for image analysis and processing through artificial intelligence. The system will evaluate images from multiple sources. As a result, a comprehensive e-Medicus system will be developed to capture and analyze X-ray data and classify cancer cells. This innovative tool, with its unique features tailored for medical facilities, will assist them in capturing and analyzing X-ray images and CT scan results. The e-Medicus system offers several benefits for medical facilities, including efficient automation of photo analysis, tracking changes in a patient's condition over time, and facilitating the identification of medical changes and data classification. A multimedia presentation of the change process will use the contour set function, allowing for topological changes in solution properties. The system integrates novel procedures and algorithms from theoretical computer science and numerical mathematics, leveraging neural networks, genetic algorithms, semantic networks, image ontology, rough set theory, contour set methods, and hybrid algorithms. These developed algorithms enhance methods and concepts in image segmentation, gathering, transmitting, storing, extracting, and effectively presenting information.

Abstract This study explores the practical applications of artificial intelligence (AI) in medical imaging, focusing on machine learning classifiers and deep learning models. The aim is to improve detection processes and diagnose diseases effectively. The study emphasizes the importance of teamwork in harnessing AI’s full potential for image analysis. Collaboration between doctors and AI experts is crucial for developing AI tools that bridge the gap between concepts and practical applications. The study demonstrates the effectiveness of machine learning classifiers, such as forest algorithms and deep learning models, in image analysis. These techniques enhance accuracy and expedite image analysis, aiding in the development of accurate medications. The study evidenced that technologically assisted medical image analysis significantly improves efficiency and accuracy across various imaging modalities, including X-ray, ultrasound, CT scans, MRI, etc. The outcomes were supported by the reduced diagnosis time. The exploration also helps us to understand the ethical considerations related to the privacy and security of data, bias, and fairness in algorithms, as well as the role of medical consultation in ensuring responsible AI use in healthcare.

In the domain of clinical imaging, the exact and quick identification proof of bone fractures assumes a crucial part in a pivotal role in facilitating timely and effective patient care. This research tends to this basic need by harnessing the force of profound learning, explicitly utilizing a Convolutional Neural Network (CNN) model as the foundation of our technique. The essential target of our study was to improve the mechanized recognition of bone fractures in X-ray images, utilizing the capacities of deep learning algorithms. The use of a CNN model permitted us to successfully capture and learn intricate patterns and features within the X-ray images, empowering the framework to make exact fracture detections. The training process included presenting the model to a various dataset, guaranteeing its versatility to an extensive variety of fracture types. The results of our research show the excellent performance of the CNN model in fracture detection, where our model has achieved an Average Precision 89.5%, Average Recall 87%, and the overall Accuracy 91%. These metrics assert the vigour of our methodology and highlight the capability of deep learning in medical image analysis.

This systematic literature review examines state-of-the-art Explainable Artificial Intelligence (XAI) methods applied to medical image analysis, discussing current challenges and future research directions, and exploring evaluation metrics used to assess XAI approaches. With the growing efficiency of Machine Learning (ML) and Deep Learning (DL) in medical applications, there's a critical need for adoption in healthcare. However, their “black-box” nature, where decisions are made without clear explanations, hinders acceptance in clinical settings where decisions have significant medicolegal consequences. Our review highlights the advanced XAI methods, identifying how they address the need for transparency and trust in ML/DL decisions. We also outline the challenges faced by these methods and propose future research directions to improve XAI in healthcare.This paper aims to bridge the gap between cutting-edge computational techniques and their practical application in healthcare, nurturing a more transparent, trustworthy, and effective use of AI in medical settings. The insights guide both research and industry, promoting innovation and standardisation in XAI implementation in healthcare.

With the advent of digital pathology and microscopic systems that can scan and save whole slide histological images automatically, there is a growing trend to use computerized methods to analyze acquired images. Among different histopathological image analysis tasks, nuclei instance segmentation plays a fundamental role in a wide range of clinical and research applications. While many semi- and fully-automatic computerized methods have been proposed for nuclei instance segmentation, deep learning (DL)-based approaches have been shown to deliver the best performances. However, the performance of such approaches usually degrades when tested on unseen datasets.In this work, we propose a novel method to improve the generalization capability of a DL-based automatic segmentation approach. Besides utilizing one of the state-of-the-art DL-based models as a baseline, our method incorporates non-deterministic train time and deterministic test time stain normalization, and ensembling to boost the segmentation performance. We trained the model with one single training set and evaluated its segmentation performance on seven test datasets. Our results show that the proposed method provides up to 4.9%, 5.4%, and 5.9% better average performance in segmenting nuclei based on Dice score, aggregated Jaccard index, and panoptic quality score, respectively, compared to the baseline segmentation model.

The identification and region extraction of brain tumors is an essential aspect of clinical image analysis and the diagnosis of brain-related illnesses. The precise and accurate identification of tumors from MRI images is particularly significant in the effective formulating of treatments such as surgery, radiation therapy, and drug therapy. The challenge of segmentation stems from the variability in the size, location, and appearance of tumors, making it a complex task. Various segmentation and classification techniques have been created and designed for brain tumor diagnosis; however, these traditional techniques are time-consuming and subjective and require expertise in image processing. In recent times, deep learning-based approaches have shown promising results in brain tumor segmentation. This research aims to develop a brain tumor segmentation and classification model that enables medical professionals to locate and measure tumors accurately and develop effective treatment and rehabilitation strategies. The process involves segmenting the tumor and further classifying it into its two major types. The parameter estimation from the segmented output provides an insight that is pivotal in the evaluation of MRI brain tumors. With further research and development, deep learning-based segmentation and classification could become an important tool for accurate detection and evaluation of brain tumors. The development of deep learning-based segmentation and classification methods can greatly benefit the medical community, and according to the finding from the experiment, it is shown that the proposed framework excels in brain tumor segmentation and classification with an accuracy of 99.3%. [ABSTRACT FROM AUTHOR]

Osteoporosis (OP) is the most prevalent and common bone disease, especially knee osteoporosis. It significantly disables sufferers all over the world. Although laborious and prone to user variation, manual diagnosis, segmentation, and annotation of knee joints continue to be the preferred way to diagnose OP in clinical procedures. Therefore, many deep learning algorithms, particularly the convolutional neural network (CNN), have been created to increase clinical workflow efficiency to overcome the shortcomings of the widely used method as above. Medical imaging procedures can show hidden structures in a volumetric view, particularly those that generate three-dimensional (3D) pictures like MRI. We created a dataset of 240 pictures from patients who had knee X-rays and skeletal bone mineral density assessments at the same time. Four convolutional neural networks (CNN) models were used to analyse the X-ray images and deep neural networks for clinical covariances to determine the degree of osteoporosis. Additionally, we investigated ensemble models that included each CNN with a clinical covariance. For every network, scores for accuracy and error rate were computed. ResNet and Alexnet displayed the highest levels of accuracy when the CNN models were tested using knee X-rays with normal, low BMD, and osteoporosis. An ensemble of DNN with Alexnet, ResNet, and both ResNet and Alexnet are employed resulting in improved accuracy. The ensemble of best-performing CNN and DNN is proposed to diagnose osteoporosis more accurately. The proposed method has produced a highly accurate osteoporosis diagnosis. [ABSTRACT FROM AUTHOR]

Recognizing and analyzing medical images is crucial for disease early detection and treatment planning with appropriate treatment options based on the patient's individual needs and disease history. Deep learning technologies are widely used in the field of healthcare because they can analyze images rapidly and precisely. However, because each object on the image has the potential to hold illness information in medical images, it is critical to analyze the images with minimal information loss. In this context, Capsule Network (CapsNet) architecture is an important approach that aims to reduce information loss by storing the location and properties of objects in images as capsules. However, because CapsNet maintains information on each object in the image, the existence of several objects in complicated images can impair CapsNet's performance. This work proposes a new model called HMedCaps to improve the performance of CapsNet. In the proposed model, it is aimed to develop a deeper and hybrid structure by using Residual Block and FractalNet module together in the feature extraction layer. While it is aimed to obtain rich feature maps by increasing the number of features extracted by deepening the network, it is aimed to prevent the vanishing gradient problem that may occur in the network with increasing depth with these modules with skip connections. Furthermore, a new squash function is proposed to make distinctive capsules more prominent by customizing capsule activation. The CIFAR10 dataset of complex images, RFMiD dataset of retinal images, and Blood Cell Count Dataset dataset of blood cell images were used to evaluate the study. When the proposed model was compared with the basic CapsNet and studies in the literature, it was observed that the performance in complex images was improved and more accurate classification results were obtained in the field of medical image analysis. The proposed hybrid HMedCaps architecture has the potential to make more accurate diagnoses in the field of medical image analysis. [ABSTRACT FROM AUTHOR]

Background/purpose: Rapid advancements in AI technology have led to significant interest in its application across various fields, including medicine and dentistry. This study aimed to assess the capabilities of ChatGPT-4V with image recognition in answering image-based questions from the Japanese National Dental Examination (JNDE) to explore its potential as an educational support tool for dental students. Materials and methods: The dataset used questions from the JNDE, which was conducted in January 2023, with a focus on image-related queries. ChatGPT-4V was utilized, and standardized prompts, question texts, and images were input. Data and statistical analyses were conducted using Qlik Sense® and GraphPad Prism. Results: The overall correct response rate of ChatGPT-4V for image-based JNDE questions was 35.0 %. The correct response rates were 57.1 % for compulsory questions, 43.6 % for general questions, and 28.6 % for clinical practical questions. In specialties like Dental Anesthesiology and Endodontics, ChatGPT-4V achieved correct response rates above 70 %, while response rates for Orthodontics and Oral Surgery were lower. A higher number of images in questions was correlated with lower accuracy, suggesting an impact of the number of images on correct and incorrect responses. Conclusion: While innovative, ChatGPT-4V’s image recognition feature exhibited limitations, especially in handling image-intensive and complex clinical practical questions, and is not yet fully suitable as an educational support tool for dental students at its current stage. Further technological refinement and re-evaluation with a broader dataset are recommended.

The cardiothoracic ratio is one of the main screening tools for heart health. Cardiothoracic ratio is usually measured manually by a cardiologist or radiologist. In the era of neural networks, which are currently developing very rapidly, we can help doctors automate and improve this process. The use of deep learning for image segmentation has proven itself as a tool that can significantly accelerate and improve the process of medical automation. In this paper, a comparative analysis of the use of several neural networks for the segmentation of the lungs and heart on X-ray images was carried out for further improvement of the automatic calculation of the cardiothoracic ratio. Using a sample of 10 test images, manual cardiothoracic ratio measurements and 7 automatic measurement options were performed. The average accuracy of the measurement of the cardiothoracic ratio of the best of the two neural networks is 93.80%, and the method that used the ensemble of networks obtained a result of 97.15%, with the help of the ensemble of neural networks it was possible to improve the ratio determination by 3.35%. The obtained results indicate that thanks to the use of an ensemble of neural networks, it was possible to improve the result of automatic measurement, and also testify to the effectiveness and prospects of using this method in the medical field

This paper presents a comparative analysis of web-based machine learning models, specifically examining Google Vertex AI, Google Teachable Machine, Azure Machine Learning and Salesforce Einstein Vision. The objective is to assess their suitability for integration into a medical information system as a classification module for medical images. The comparative evaluation considers factors such as model accuracy, ease of integration and scalability. The findings aim to guide the selection of an optimal machine learning solution for enhancing the capabilities of medical image classification within a healthcare context.

Abstract Breast cancer is a major public health concern, and early detection and classification are essential for improving patient outcomes. However, breast tumors can be difficult to distinguish from benign tumors, leading to high false positive rates in screening. The reason is that both benign and malignant tumors have no consistent shape, are found at the same position, have variable sizes, and have high correlations. The ambiguity of the correlation challenges the computer-aided system, and the inconsistency of morphology challenges an expert in identifying and classifying what is positive and what is negative. Due to this, most of the time, breast cancer screen is prone to false positive rates. This research paper presents the introduction of a feature enhancement method into the Google inception network for breast cancer detection and classification. The proposed model preserves both local and global information, which is important for addressing the variability of breast tumor morphology and their complex correlations. A locally preserving projection transformation function is introduced to retain local information that might be lost in the intermediate output of the inception model. Additionally, transfer learning is used to improve the performance of the proposed model on limited datasets. The proposed model is evaluated on a dataset of ultrasound images and achieves an accuracy of 99.81%, recall of 96.48%, and sensitivity of 93.0%. These results demonstrate the effectiveness of the proposed method for breast cancer detection and classification.

Abstract Background Chest X-ray imaging based abnormality localization, essential in diagnosing various diseases, faces significant clinical challenges due to complex interpretations and the growing workload of radiologists. While recent advances in deep learning offer promising solutions, there is still a critical issue of domain inconsistency in cross-domain transfer learning, which hampers the efficiency and accuracy of diagnostic processes. This study aims to address the domain inconsistency problem and improve autonomic abnormality localization performance of heterogeneous chest X-ray image analysis, particularly in detecting abnormalities, by developing a self-supervised learning strategy called “BarlwoTwins-CXR”. Methods We utilized two publicly available datasets: the NIH Chest X-ray Dataset and the VinDr-CXR. The BarlowTwins-CXR approach was conducted in a two-stage training process. Initially, self-supervised pre-training was performed using an adjusted Barlow Twins algorithm on the NIH dataset with a Resnet50 backbone pre-trained on ImageNet. This was followed by supervised fine-tuning on the VinDr-CXR dataset using Faster R-CNN with Feature Pyramid Network (FPN). The study employed mean Average Precision (mAP) at an Intersection over Union (IoU) of 50% and Area Under the Curve (AUC) for performance evaluation. Results Our experiments showed a significant improvement in model performance with BarlowTwins-CXR. The approach achieved a 3% increase in mAP50 accuracy compared to traditional ImageNet pre-trained models. In addition, the Ablation CAM method revealed enhanced precision in localizing chest abnormalities. The study involved 112,120 images from the NIH dataset and 18,000 images from the VinDr-CXR dataset, indicating robust training and testing samples. Conclusion BarlowTwins-CXR significantly enhances the efficiency and accuracy of chest X-ray image-based abnormality localization, outperforming traditional transfer learning methods and effectively overcoming domain inconsistency in cross-domain scenarios. Our experiment results demonstrate the potential of using self-supervised learning to improve the generalizability of models in medical settings with limited amounts of heterogeneous data. This approach can be instrumental in aiding radiologists, particularly in high-workload environments, offering a promising direction for future AI-driven healthcare solutions.

Abstract Brain tumor classification using MRI images is a crucial yet challenging task in medical imaging. Accurate diagnosis is vital for effective treatment planning but is often hindered by the complex nature of tumor morphology and variations in imaging. Traditional methodologies primarily rely on manual interpretation of MRI images, supplemented by conventional machine learning techniques. These approaches often lack the robustness and scalability needed for precise and automated tumor classification. The major limitations include a high degree of manual intervention, potential for human error, limited ability to handle large datasets, and lack of generalizability to diverse tumor types and imaging conditions.To address these challenges, we propose a federated learning-based deep learning model that leverages the power of Convolutional Neural Networks (CNN) for automated and accurate brain tumor classification. This innovative approach not only emphasizes the use of a modified VGG16 architecture optimized for brain MRI images but also highlights the significance of federated learning and transfer learning in the medical imaging domain. Federated learning enables decentralized model training across multiple clients without compromising data privacy, addressing the critical need for confidentiality in medical data handling. This model architecture benefits from the transfer learning technique by utilizing a pre-trained CNN, which significantly enhances its ability to classify brain tumors accurately by leveraging knowledge gained from vast and diverse datasets.Our model is trained on a diverse dataset combining figshare, SARTAJ, and Br35H datasets, employing a federated learning approach for decentralized, privacy-preserving model training. The adoption of transfer learning further bolsters the model’s performance, making it adept at handling the intricate variations in MRI images associated with different types of brain tumors. The model demonstrates high precision (0.99 for glioma, 0.95 for meningioma, 1.00 for no tumor, and 0.98 for pituitary), recall, and F1-scores in classification, outperforming existing methods. The overall accuracy stands at 98%, showcasing the model’s efficacy in classifying various tumor types accurately, thus highlighting the transformative potential of federated learning and transfer learning in enhancing brain tumor classification using MRI images.

Abstract A prompt diagnosis of breast cancer in its earliest phases is necessary for effective treatment. While Computer-Aided Diagnosis systems play a crucial role in automated mammography image processing, interpretation, grading, and early detection of breast cancer, existing approaches face limitations in achieving optimal accuracy. This study addresses these limitations by hybridizing the improved quantum-inspired binary Grey Wolf Optimizer with the Support Vector Machines Radial Basis Function Kernel. This hybrid approach aims to enhance the accuracy of breast cancer classification by determining the optimal Support Vector Machine parameters. The motivation for this hybridization lies in the need for improved classification performance compared to existing optimizers such as Particle Swarm Optimization and Genetic Algorithm. Evaluate the efficacy of the proposed IQI-BGWO-SVM approach on the MIAS dataset, considering various metric parameters, including accuracy, sensitivity, and specificity. Furthermore, the application of IQI-BGWO-SVM for feature selection will be explored, and the results will be compared. Experimental findings demonstrate that the suggested IQI-BGWO-SVM technique outperforms state-of-the-art classification methods on the MIAS dataset, with a resulting mean accuracy, sensitivity, and specificity of 99.25%, 98.96%, and 100%, respectively, using a tenfold cross-validation datasets partition.

Abstract Advancements in clinical treatment are increasingly constrained by the limitations of supervised learning techniques, which depend heavily on large volumes of annotated data. The annotation process is not only costly but also demands substantial time from clinical specialists. Addressing this issue, we introduce the S4MI (Self-Supervision and Semi-Supervision for Medical Imaging) pipeline, a novel approach that leverages advancements in self-supervised and semi-supervised learning. These techniques engage in auxiliary tasks that do not require labeling, thus simplifying the scaling of machine supervision compared to fully-supervised methods. Our study benchmarks these techniques on three distinct medical imaging datasets to evaluate their effectiveness in classification and segmentation tasks. Notably, we observed that self-supervised learning significantly surpassed the performance of supervised methods in the classification of all evaluated datasets. Remarkably, the semi-supervised approach demonstrated superior outcomes in segmentation, outperforming fully-supervised methods while using 50% fewer labels across all datasets. In line with our commitment to contributing to the scientific community, we have made the S4MI code openly accessible, allowing for broader application and further development of these methods. The code can be accessed at https://github.com/pranavsinghps1/S4MI .

Retinoblastoma is a kind of malignant eye tumor commonly seen in children, which is one of the main causes threatening children's vision and life. The diagnosis and evaluation of retinoblastoma has always been a hot topic in clinic. In the past few years, the application of artificial intelligence(AI)technology has made significant progress in the medical field, providing new opportunities and challenges for the diagnosis and treatment of retinoblastoma, for example, the use of AI algorithms to analyze massive clinical data, which can help doctors diagnose the disease more accurately and provide personalized treatment plans. In addition, AI technology also plays an important role in medical image analysis, genomics research and other aspects, which can help the development of new drugs and improve patient prognosis. This article reviews the application progress of AI in retinoblastoma.

Background Purpose Methods Results Conclusions Deep learning technology has made remarkable progress in pancreatic image segmentation tasks. However, annotating 3D medical images is time‐consuming and requires expertise, and existing semi‐supervised segmentation methods perform poorly in the segmentation task of organs with blurred edges in enhanced CT such as the pancreas.To address the challenges of limited labeled data and indistinct boundaries of regions of interest (ROI).We propose Edge‐Biased Consistency Regularization (EBC‐Net). 3D edge detection is employed to construct edge perturbations and integrate edge prior information into limited data, aiding the network in learning from unlabeled data. Additionally, due to the one‐sidedness of a single perturbation space, we expand the dual‐level perturbation space of both images and features to more efficiently focus the model's attention on the edges of the ROI. Finally, inspired by the clinical habits of doctors, we propose a 3D Anatomical Invariance Extraction Module and Anatomical Attention to capture anatomy‐invariant features.Extensive experiments have demonstrated that our method outperforms state‐of‐the‐art methods in semi‐supervised pancreas image segmentation. Moreover, it can better preserve the morphology of pancreatic organs and excel at edges region accuracy.Incorporated with edge prior knowledge, our method mixes disturbances in dual‐perturbation space, which shifts the network's attention to the fuzzy edge region using a few labeled samples. These ideas have been verified on the pancreas segmentation dataset. [ABSTRACT FROM AUTHOR]

Attacks on deep learning models are a constant threat in the world today. As more deep learning models and artificial intelligence (AI) are being implemented across different industries, the likelihood of them being attacked increases dramatically. In this context, the medical domain is of the greatest concern because an erroneous decision made by AI could have a catastrophic outcome and even lead to death. Therefore, a systematic procedure is built in this study to determine how well these medical images can resist a specific adversarial attack, i.e. a one-pixel attack. This may not be the strongest attack, but it is simple and effective, and it could occur by accident or an equipment malfunction. The results of the experiment show that it is difficult for medical images to survive a one-pixel attack. [ABSTRACT FROM AUTHOR]

Ultrasound imaging has a history of several decades. With its non-invasive, low-cost advantages, this technology has been widely used in medicine and there have been many significant breakthroughs in ultrasound imaging. Even so, there are still some drawbacks. Therefore, some novel image reconstruction and image analysis algorithms have been proposed to solve these problems. Although these new solutions have some effects, many of them introduce some other side effects, such as high computational complexity in beamforming. At the same time, the usage requirements of medical ultrasound equipment are relatively high, and it is not very user- friendly for inexperienced beginners. As artificial intelligence technology advances, some researchers have initiated efforts to deploy deep learning to address challenges in ultrasound imaging, such as reducing computational complexity in adaptive beamforming and aiding novices in image acquisition. In this survey, we are about to explore the application of deep learning in medical ultrasound imaging, spanning from image reconstruction to clinical diagnosis. [ABSTRACT FROM AUTHOR]

White blood cell (WBC) classification is pivotal in medical image analysis, playing a critical role in the precise diagnosis and monitoring of diseases. This paper presents a novel convolutional neural network (CNN) architecture designed specifically for the classification of WBC images. Our model, trained on an extensive dataset, automates the extraction of discriminative features essential for accurate subtype identification. We conducted comprehensive experiments on a publicly available image dataset to validate the efficacy of our methodology. Comparative analysis with state-of-the-art methods shows that our approach significantly outperforms existing models in accurately categorizing WBCs into their respective subtypes. An in-depth analysis of the features learned by the CNN reveals key insights into the morphological traits—such as shape, size, and texture—that contribute to its classification accuracy. Importantly, the model demonstrates robust generalization capabilities, suggesting its high potential for real-world clinical implementation. Our findings indicate that the proposed CNN architecture can substantially enhance the precision and efficiency of WBC subtype identification, offering significant improvements in medical diagnostics and patient care. [ABSTRACT FROM AUTHOR]

In the field of medical image analysis, object detection plays a crucial role by providing interpretable diagnostic information to healthcare professionals. Although current object detection models have achieved remarkable success in conventional images, their performance in detecting abnormalities in medical images has not been as satisfactory. This is primarily due to the complexity of anatomical structures in medical images, and the fact that some lesions may have subtle features, particularly in the case of early‐stage, small‐scale abnormalities. To address this challenge, we introduce SOCR‐YOLO, a novel lesion detection model with online convolutional reparameterization based on channel shuffling. First, it employs the SOCR (Shuffled Channel with Online Convolutional Re‐parameterization) module to establish a connection between feature concatenation and computational efficiency, aiming to extract more comprehensive information while reducing time consumption. Second, it incorporates the Bi‐FPN structure to achieve multiscale feature fusion. Lastly, the loss function has been optimized to improve the model training process. We evaluated two datasets, chest x‐ray (Vindr‐CXR) and brain tumor (Br35H), provided by the Kaggle competition. Experimental results show that the proposed method has outperformed several state‐of‐the‐art models, including YOLOv8, YOLO‐NAS, and RT‐DETR, in both speed and accuracy. Notably, in the context of chest x‐ray anomaly detection, SOCR‐YOLO exhibits a 1.8% enhancement in accuracy over YOLOv8 while simultaneously reducing floating‐point operations by 26.3%. Additionally, a similar 1.8% improvement in accuracy is observed in the detection of brain tumors. The results indicate the superior ability of our model to detect multiscale variations and small lesions. [ABSTRACT FROM AUTHOR]

Numerous advancements and significant progress have been made in computer methods for medical applications, alongside technological developments. Automatic image analysis plays a crucial role in the realm of medical diagnosis and therapy. Recent breakthroughs, especially in the field of medical image processing, have enabled the automatic detection of various characteristics, alterations, diseases, and degenerative conditions using skin scans. Utilizing image processing methods, skin image analysis is instrumental in the identification and monitoring of conditions manifesting through alterations in skin structure. Notably, accurate segmentation of cancerous regions from the background remains a challenging task in the area of melanoma image analysis. The primary objective of this study is to achieve exceptional precision in delineating melanoma boundaries. Leveraging the Bat Optimization algorithm, we determine the optimal threshold for melanoma segmentation, effectively identifying the most accurate cancerous area boundaries. To evaluate the results, standard metrics such as accuracy, sensitivity, specificity, Dice coefficient, and F1 score are employed. In this study, we applied the Bat Optimization algorithm to determine the optimal threshold value for segmenting melanoma skin cancer, effectively identifying the most accurate cancerous area boundaries. For result evaluation, we employed standard metrics including accuracy, sensitivity, specificity, Dice coefficient, and F1 score, which yielded impressive values of 99.8%, 98.99%, 98.87%, 98.45%, and 98.24%, respectively. [ABSTRACT FROM AUTHOR]

In this study, we investigated the discriminative capacity of knee morphology in automatic detection of osteophytes defined by the Osteoarthritis Research Society International atlas, using X‐ray and magnetic resonance imaging (MRI) data. For the X‐ray analysis, we developed a deep learning (DL) based model to segment femur and tibia. In case of MRIs, we utilized previously validated segmentations of femur, tibia, corresponding cartilage tissues, and menisci. Osteophyte detection was performed using DL models in four compartments: medial femur (FM), lateral femur (FL), medial tibia (TM), and lateral tibia (TL). To analyze the confounding effects of soft tissues, we investigated their morphology in combination with bones, including bones+cartilage, bones+menisci, and all the tissues. From X‐ray‐based 2D morphology, the models yielded balanced accuracy of 0.73, 0.69, 0.74, and 0.74 for FM, FL, TM, TL, respectively. Using 3D bone morphology from MRI, balanced accuracy was 0.80, 0.77, 0.71, and 0.76, respectively. The performance was higher than in 2D for all the compartments except for TM, with significant improvements observed for femoral compartments. Adding menisci or cartilage morphology consistently improved balanced accuracy in TM, with the greatest improvement seen for small osteophyte. Otherwise, the models performed similarly to bones‐only. Our experiments demonstrated that MRI‐based models show higher detection capability than X‐ray based models for identifying knee osteophytes. This study highlighted the feasibility of automated osteophyte detection from X‐ray and MRI data and suggested further need for development of osteophyte assessment criteria in addition to OARSI, particularly, for early osteophytic changes. [ABSTRACT FROM AUTHOR]

Rapid advancements in AI technology have led to significant interest in its application across various fields, including medicine and dentistry. This study aimed to assess the capabilities of ChatGPT-4V with image recognition in answering image-based questions from the Japanese National Dental Examination (JNDE) to explore its potential as an educational support tool for dental students. The dataset used questions from the JNDE, which was conducted in January 2023, with a focus on image-related queries. ChatGPT-4V was utilized, and standardized prompts, question texts, and images were input. Data and statistical analyses were conducted using Qlik Sense® and GraphPad Prism. The overall correct response rate of ChatGPT-4V for image-based JNDE questions was 35.0 %. The correct response rates were 57.1 % for compulsory questions, 43.6 % for general questions, and 28.6 % for clinical practical questions. In specialties like Dental Anesthesiology and Endodontics, ChatGPT-4V achieved correct response rates above 70 %, while response rates for Orthodontics and Oral Surgery were lower. A higher number of images in questions was correlated with lower accuracy, suggesting an impact of the number of images on correct and incorrect responses. While innovative, ChatGPT-4V's image recognition feature exhibited limitations, especially in handling image-intensive and complex clinical practical questions, and is not yet fully suitable as an educational support tool for dental students at its current stage. Further technological refinement and re-evaluation with a broader dataset are recommended. [ABSTRACT FROM AUTHOR]