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Purpose: This systematic review examines the utility of deep learning algorithms in predicting pathological complete response (pCR) in rectal cancer patients undergoing neoadjuvant chemoradiotherapy (nCRT). The primary goal is to evaluate the performance of MRI-based artificial intelligence (AI) models and explore factors affecting their diagnostic accuracy. Methods: The review followed PRISMA guidelines and is registered with PROSPERO (CRD42024628017). Literature searches were conducted in PubMed, Embase, and Cochrane Library using keywords such as "artificial intelligence," "rectal cancer," "MRI," and "pathological complete response." Articles involving deep learning models applied to MRI for predicting pCR were included, excluding non-MRI data and studies without AI applications. Data on study characteristics, MRI sequences, AI model details, and performance metrics were extracted. Quality assessment was performed using the PROBAST tool. Results: Out of 512 initial records, 26 studies met the inclusion criteria. Most studies demonstrated promising diagnostic performance, with AUC values for external validation typically exceeding 0.8. The use of T2W and diffusion-weighted imaging (DWI) MRI phases enhanced model accuracy compared to T2W alone. Larger datasets generally correlated with improved model performance. However, heterogeneity in model designs, MRI protocols, and the limited integration of clinical data were noted as challenges. Conclusion: AI-enhanced MRI demonstrates significant potential in predicting pCR in rectal cancer, particularly with T2W + DWI sequences and larger datasets. While integrating clinical data remains controversial, standardizing methodologies and expanding datasets will further enhance model robustness and clinical utility. [ABSTRACT FROM AUTHOR]

Background: The large language model ChatGPT can now accept image input with the GPT4-vision (GPT4V) version. We aimed to compare the performance of GPT4V to pretrained U-Net and vision transformer (ViT) models for the identification of the progression of multiple sclerosis (MS) on magnetic resonance imaging (MRI). Methods: Paired coregistered MR images with and without progression were provided as input to ChatGPT4V in a zero-shot experiment to identify radiologic progression. Its performance was compared to pretrained U-Net and ViT models. Accuracy was the primary evaluation metric and 95% confidence interval (CIs) were calculated by bootstrapping. We included 170 patients with MS (50 males, 120 females), aged 21–74 years (mean 42.3), imaged at a single institution from 2019 to 2021, each with 2–5 MRI studies (496 in total). Results: One hundred seventy patients were included, 110 for training, 30 for tuning, and 30 for testing; 100 unseen paired images were randomly selected from the test set for evaluation. Both U-Net and ViT had 94% (95% CI: 89–98%) accuracy while GPT4V had 85% (77–91%). GPT4V gave cautious nonanswers in six cases. GPT4V had precision (specificity), recall (sensitivity), and F1 score of 89% (75–93%), 92% (82–98%), 91 (82–97%) compared to 100% (100–100%), 88 (78–96%), and 0.94 (88–98%) for U-Net and 94% (87–100%), 94 (88–100%), and 94 (89–98%) for ViT. Conclusion: The performance of GPT4V combined with its accessibility suggests has the potential to impact AI radiology research. However, misclassified cases and overly cautious non-answers confirm that it is not yet ready for clinical use. Relevance statement: GPT4V can identify the radiologic progression of MS in a simplified experimental setting. However, GPT4V is not a medical device, and its widespread availability highlights the need for caution and education for lay users, especially those with limited access to expert healthcare. Key Points: Without fine-tuning or the need for prior coding experience, GPT4V can perform a zero-shot radiologic change detection task with reasonable accuracy. However, in absolute terms, in a simplified "spot the difference" medical imaging task, GPT4V was inferior to state-of-the-art computer vision methods. GPT4V's performance metrics were more similar to the ViT than the U-net. This is an exploratory experimental study and GPT4V is not intended for use as a medical device. [ABSTRACT FROM AUTHOR]

Background: Considering the disruptive potential of AI technology, its current and future impact in healthcare, as well as healthcare professionals' lack of training in how to use it, the paper summarizes how to approach the challenges of AI from an ethical and legal perspective. It concludes with suggestions for improvements to help healthcare professionals better navigate the AI wave. Methods: We analyzed the literature that specifically discusses ethics and law related to the development and implementation of AI in healthcare as well as relevant normative documents that pertain to both ethical and legal issues. After such analysis, we created categories regrouping the most frequently cited and discussed ethical and legal issues. We then proposed a breakdown within such categories that emphasizes the different - yet often interconnecting - ways in which ethics and law are approached for each category of issues. Finally, we identified several key ideas for healthcare professionals and organizations to better integrate ethics and law into their practices. Results: We identified six categories of issues related to AI development and implementation in healthcare: (1) privacy; (2) individual autonomy; (3) bias; (4) responsibility and liability; (5) evaluation and oversight; and (6) work, professions and the job market. While each one raises different questions depending on perspective, we propose three main legal and ethical priorities: education and training of healthcare professionals, offering support and guidance throughout the use of AI systems, and integrating the necessary ethical and legal reflection at the heart of the AI tools themselves. Conclusions: By highlighting the main ethical and legal issues involved in the development and implementation of AI technologies in healthcare, we illustrate their profound effects on professionals as well as their relationship with patients and other organizations in the healthcare sector. We must be able to identify AI technologies in medical practices and distinguish them by their nature so we can better react and respond to them. Healthcare professionals need to work closely with ethicists and lawyers involved in the healthcare system, or the development of reliable and trusted AI will be jeopardized. [ABSTRACT FROM AUTHOR]

Laparoscopic exploration (LE) is crucial for diagnosing intra-abdominal metastasis (IAM) in advanced gastric cancer (GC). However, overlooking single, tiny, and occult IAM lesions during LE can severely affect the treatment and prognosis due to surgeons' visual misinterpretations. To address this, we developed the artificial intelligence laparoscopic exploration system (AiLES) to recognize IAM lesions with various metastatic extents and locations. The AiLES was developed based on a dataset consisting of 5111 frames from 100 videos, using 4130 frames for model development and 981 frames for evaluation. The AiLES achieved a Dice score of 0.76 and a recognition speed of 11 frames per second, demonstrating robust performance in different metastatic extents (0.74–0.76) and locations (0.63–0.90). Furthermore, AiLES performed comparably to novice surgeons in IAM recognition and excelled in recognizing tiny and occult lesions. Our results demonstrate that the implementation of AiLES could enhance accurate tumor staging and assist individualized treatment decisions. [ABSTRACT FROM AUTHOR]

This study utilizes the Breast Ultrasound Image (BUSI) dataset to present a deep learning technique for breast tumor segmentation based on a modified UNet architecture. To improve segmentation accuracy, the model integrates attention mechanisms, such as the Convolutional Block Attention Module (CBAM) and Non-Local Attention, with advanced encoder architectures, including ResNet, DenseNet, and EfficientNet. These attention mechanisms enable the model to focus more effectively on relevant tumor areas, resulting in significant performance improvements. Models incorporating attention mechanisms outperformed those without, as reflected in superior evaluation metrics. The effects of Dice Loss and Binary Cross-Entropy (BCE) Loss on the model's performance were also analyzed. Dice Loss maximized the overlap between predicted and actual segmentation masks, leading to more precise boundary delineation, while BCE Loss achieved higher recall, improving the detection of tumor areas. Grad-CAM visualizations further demonstrated that attention-based models enhanced interpretability by accurately highlighting tumor areas. The findings denote that combining advanced encoder architectures, attention mechanisms, and the UNet framework can yield more reliable and accurate breast tumor segmentation. Future research will explore the use of multi-modal imaging, real-time deployment for clinical applications, and more advanced attention mechanisms to further improve segmentation performance. [ABSTRACT FROM AUTHOR]

Osteoarthritis (OA) is a musculoskeletal disorder that affects weight-bearing joints like the hip, knee, spine, feet, and fingers. It is a chronic disorder that causes joint stiffness and leads to functional impairment. Knee osteoarthritis (KOA) is a degenerative knee joint disease that is a significant disability for over 60 years old, with the most prevalent symptom of knee pain. Radiography is the gold standard for the evaluation of KOA. These radiographs are evaluated using different classification systems. Kellgren and Lawrence's (KL) classification system is used to classify X-rays into five classes (Normal = 0 to Severe = 4) based on osteoarthritis severity levels. In recent years, with the advent of artificial intelligence, machine learning, and deep learning, more emphasis has been given to automated medical diagnostic systems or decision support systems. Computer-aided diagnosis is needed for the improvement of health-related information systems. This survey aims to review the latest advances in automated radiographic classification and detection of KOA using the KL system. A total of 85 articles are reviewed as original research or survey articles. This survey will benefit researchers, practitioners, and medical experts interested in X-rays-based KOA diagnosis and prediction. [ABSTRACT FROM AUTHOR]

Medical image segmentation constitutes a crucial step in the analysis of medical images, possessing extensive applications and research significance within the realm of medical research and practice. Convolutional neural network achieved great success in medical image segmentation. However, acquiring large labeled datasets remains unattainable due to the substantial expertise and time required for image labeling, as well as heightened patient privacy concerns. To solve scarce medical image data, we propose a powerful network Domain Tuning SAM for Medical images (DT-SAM). We construct an encoder utilizing a parameter-effective fine-tuning strategy and SAM. This strategy selectively updates a small fraction of the weight increments while preserving the majority of the pre-training weights in the SAM encoder, consequently reducing the required number of training samples. Meanwhile, our approach leverages only SAM encoder structure while incorporating a decoder similar to U-Net decoder structure and redesigning skip connections to concatenate encoder-extracted features, which effectively decode the features extracted by the encoder and preserve edge information. We have conducted comprehensive experiments on three publicly available medical image segmentation datasets. The combined experimental results show that our method can effectively perform few shot medical image segmentation. With just one labeled data, achieving a Dice score of 63.51%, a HD of 17.94 and an IoU score of 73.55% on Heart Task, on Prostate Task, an average Dice score of 46.01%, a HD of 10.25 and an IoU score of 65.92% were achieved, and the Dice, HD, and IoU score reaching 88.67%, 10.63, and 90.19% on BUSI. Remarkably, with few training samples, our method consistently outperforms various based on SAM and CNN. [ABSTRACT FROM AUTHOR]

As the demand for tunnel inspection grows, visual methods are being utilized, but traditional machine learning techniques struggle with continuous learning and updating prototype classes without repeated defect data acquisition. Incremental learning provides a way to address catastrophic forgetting, but conventional methods often cannot handle the imbalanced data typical in real-world situations. This paper proposes a two-stage learning paradigm specifically designed for incremental learning to reduce overfitting from head data in unbalanced classes. Our approach maximizes symmetric separation of inter-class prototypes in the classifier space by integrating DR loss within the ETF classifier. Compared to other leading incremental learning methods, our approach shows superior performance on tunneling data. [ABSTRACT FROM AUTHOR]

Multi-classification of colorectal polyps using endoscopic images is crucial for enhancing clinical diagnostic accuracy and reducing colorectal cancer mortality. Accurately classifying colorectal polyps poses significant challenges due to blurred lesion boundaries, varying intra-class scales, and high inter-class similarities. To address these challenges, we propose the Fused Residual Attention Network (FRAN) for colorectal polyp classification. FRAN employs a dual-branch structure to emphasize both semantic and detailed information. The Residual Attention Learning mechanism enhances lesion region detection, while Global Dependent Self-Attention captures global context. Additionally, the Edge Feature Fusion module, combined with Semantic Alignment, mitigates semantic loss during upsampling and captures edge-detailed features. We evaluated FRA on a private four-class colorectal polyp dataset, the three-class public Kvasir dataset, the three-class public HyperKvasir dataset, and the four-class public PICCOLO dataset. The overall classification accuracies achieved are 85.73% and 97.16%, respectively, which are higher than those of the compared state-of-the-art colorectal polyp classification algorithms. Our approach effectively highlights critical regions and maintains detailed information, thereby offering a robust solution to the challenges in colorectal polyp classification. [ABSTRACT FROM AUTHOR]

The use of breast density as a biomarker for breast cancer treatment has not been well established owing to the difficulty in measuring time-series changes in breast density. In this study, we developed a surmising model for breast density using prior mammograms through a multiple regression analysis, enabling a time series analysis of breast density. We acquired 1320 mediolateral oblique view mammograms to construct the surmising model using multiple regression analysis. The dependent variable was the breast density of the mammary gland region segmented by certified radiological technologists, and independent variables included the compressed breast thickness (CBT), exposure current times exposure second (mAs), tube voltage (kV), and patients' age. The coefficient of determination of the surmising model was 0.868. After applying the model, the correlation coefficients of the three groups based on the CBT (thin group, 18-36 mm; standard group, 38-46 mm; and thick group, 48-78 mm) were 0.913, 0.945, and 0.867, respectively, suggesting that the thick breast group had a significantly low correlation coefficient (p = 0.00231). In conclusion, breast density can be accurately surmised using the CBT, mAs, tube voltage, and patients' age, even in the absence of a mammogram image., Competing Interests: Declarations. Ethics approval: This study was approved by the ethics committee of Kindai University, Faculty of Medicine. Competing interests: The authors declare no competing interests., (© 2024. International Federation for Medical and Biological Engineering.)

Three-dimensional (3D) chromatin interactions, such as enhancer-promoter interactions (EPIs), loops, topologically associating domains (TADs), and A/B compartments, play critical roles in a wide range of cellular processes by regulating gene expression. Recent development of chromatin conformation capture technologies has enabled genome-wide profiling of various 3D structures, even with single cells. However, current catalogs of 3D structures remain incomplete and unreliable due to differences in technology, tools, and low data resolution. Machine learning methods have emerged as an alternative to obtain missing 3D interactions and/or improve resolution. Such methods frequently use genome annotation data (ChIP-seq, DNAse-seq, etc.), DNA sequencing information (k-mers and transcription factor binding site (TFBS) motifs), and other genomic properties to learn the associations between genomic features and chromatin interactions. In this review, we discuss computational tools for predicting three types of 3D interactions (EPIs, chromatin interactions, and TAD boundaries) and analyze their pros and cons. We also point out obstacles to the computational prediction of 3D interactions and suggest future research directions., (© 2025. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Purpose: Computer vision and artificial intelligence (AI) offer the opportunity to rapidly and accurately interpret standardized x-rays. We trained and validated a machine learning tool that identified key reference points and determined glenoid retroversion and glenohumeral relationships on axillary radiographs., Methods: Standardized pre and post arthroplasty axillary radiographs were manually annotated locating six reference points and used to train a computer vision model that could identify these reference points without human guidance. The model then used these reference points to determine humeroglenoid alignment in the anterior to posterior direction and glenoid version. The model's accuracy was tested on a separate test set of axillary images not used in training, comparing its reference point locations, alignment and version to the corresponding values assessed by two surgeons., Results: On the test set of pre- and post-operative images not used in the training process, the model was able to rapidly identify all six reference point locations to within a mean of 2 mm of the surgeon-assessed points. The mean variation in alignment and version measurements between the surgeon assessors and the model was similar to the variation between the two surgeon assessors., Conclusions: This article reports on the development and validation of a computer vision/artificial intelligence model that can independently identify key landmarks and determine the glenohumeral relationship and glenoid version on axillary radiographs. This observer-independent approach has the potential to enable efficient human observer independent assessment of shoulder radiographs, lessening the burden of manual x-ray interpretation and enabling scaling of these measurements across large numbers of patients from multiple centers so that pre and postoperative anatomy can be correlated with patient reported clinical outcomes., Level of Evidence: Level III Study of Diagnostic Test., Competing Interests: Declarations. Ethics approval: This study was determined to be exempt from our institution’s Human Subjects Review Committee approval process. Consent to participate: N/A. Consent to publish: N/A. Competing interests: Dr. Jason Hsu does report personal fees from Enovis and is a Committee member of the American Shoulder and Elbow Surgeons outside of the submitted work., (© 2024. The Author(s) under exclusive licence to SICOT aisbl.)