Jun 2026· Cancers· Vol 18· 0 citations· 33 references
Medicine
TL;DR
A two-stage artificial intelligence system is developed that highlights predefined suspicious features, classifies images, and shows which regions influenced its prediction of malignant biliary stricture.
Abstract
Simple Summary Malignant biliary stricture is difficult to detect early, and current tests often miss it or give uncertain answers. Doctors can pass a thin camera directly into the bile duct to inspect narrowed areas, but reading these images is challenging and varies from one specialist to another. We developed a two-stage artificial intelligence system that highlights predefined suspicious features, classifies images, and shows which regions influenced its prediction. The system was evaluated retrospectively using images and archived videos from three hospitals. External sensitivity was lower for individual frames, while patient-level estimates from only 25 external patients were imprecise. Prospective testing on uncurated live procedures is therefore required before clinical use.
Colon cancer represents a growing universal issue related to health, with prompt and accurate detection essential for indispensable to improving outcomes of people's health. Standard approaches, like colonoscopy and histopathology, while useful, tend to be invasive, time-intensive and prone to human interpretative bias. Recent developments in deep learning (DL) have facilitated the creation of automated systems that improve the precision, speed and uniformity of colon cancer diagnosis and categorisation. This paper offers a thorough comparative examination of various advanced DL models, including ResNet, DenseNet and MobileNet, applied to multi-modal imaging datasets consisting of colonoscopy images. Quantitative findings indicate exceptional accuracy, precision and recall in diagnostic tasks, with MobileNet DL models outperforming in tumour diagnosis and grading than other peer groups.
Sivakumar Rajendran· 2026 4th International Confe...· 0 citations
BACKGROUND AND OBJECTIVE
Contrast-enhanced ultrasound (CEUS) is widely used for evaluating thyroid nodule malignancy, but conventional time-intensity curve (TIC) analysis is labor-intensive and operator-dependent. This study proposes LSTAC, an automated framework for nodule segmentation and TIC analysis in CEUS videos.
METHODS
LSTAC integrates an improved YOLOv5-based segmentation network with a peak intensity frame (PIF) extraction algorithm to enable automatic nodule localization, TIC generation, and PIF identification. The framework was trained using CEUS data from 623 patients collected across three hospitals and evaluated on both internal and external validation cohorts.
RESULTS
LSTAC achieved 3-10× higher efficiency than VueBox in PIF extraction while maintaining strong temporal accuracy (0.94, 0.77, 0.79) and structural similarity (SSIM: 0.80, 0.60, 0.67). In malignancy prediction based on PIF features, LSTAC outperformed VueBox in two of three validation sets, with AUCs of 0.8279 vs. 0.8226 and 0.8000 vs. 0.7000.
CONCLUSION
LSTAC provides an efficient and accurate solution for automated CEUS analysis, reducing manual workload and improving consistency in thyroid nodule assessment, with potential for clinical application.
Aoxiang Yang, Liuyue Li, Ruifan He et al.· Artificial Intelligence in M...· 0 citations
The proposed IMIL-Net provides a high-accuracy, interpretable, and clinically-aligned diagnostic solution by analyzing complete patient examinations and providing a statistically validated decision-making process, and represents a trustworthy tool for integration into the clinical otolaryngology workflow.
Biao Xu, Miao Zhang, Shuai Jiang et al.· European archives of oto-rhi...· 0 citations
An advanced system capable of automatically detecting complicated appendicitis from ultrasound images was developed and was explained with gradient-weighted class activation mapping (Grad-CAM), which creates a heatmap of the regions responsible for the model's prediction of the infected areas.
Fahad Ahammed, Omar Faruq Shikdar, Navid Zaman et al.· 0 citations
One of the most common and deadly infectious illnesses in the world is still tuberculosis (TB), especially in developing nations with inadequate healthcare systems. In order to stop the spread of tuberculosis and enhance patient outcomes, early identification and diagnosis are essential. In this study, we present a deep learning-based system that uses chest X-ray pictures to automatically detect tuberculosis. Despite the difficulties of limited dataset availability, the system uses transfer learning using MobileNetV2 and DenseNet architectures to classify chest Xrays as either TB-positive or Healthy, reaching notable accuracy. To increase model generalisation and image quality, pre-processing methods like Contrast Limited Adaptive Histogram Equalisation (CLAHE) and sophisticated data augmentation approaches are used. The trained model is then implemented as a Flask web application, offering a user-friendly interface with features like secure login, image upload and preview, prediction results with probability scores, and performance metrics visualisation like accuracy curves, confusion matrices, and ROC curves. The suggested framework shows how deep learning can be used to create scalable, dependable, and affordable diagnostic tools to help radiologists and other medical professionals with TB screening and diagnosis.
Zoya Nasreen, Afshan Fatima, Ruqiya Fatima· International Journal of AI...· 0 citations
An attention-enhanced deep learning framework for clinically accurate pneumonia identification from chest imaging radiology that combines a self-attention mechanism with a pretrained VGG16 backbone is proposed and tested against many cutting-edge convolutional neural network architectures.
Mohini Gahlot, Pinaki Ghosh· International journal of com...· 0 citations