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The application of deep learning in early enamel demineralization detection.
PeerJ
January 2025
State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, China.
Objective: The study aims to develop a diagnostic model using intraoral photographs to accurately detect and classify early detection of enamel demineralization on tooth surfaces.
Methods: A retrospective analysis was conducted with 208 patients aged 14 to 44. A total of 624 high-quality digital images captured under standardized conditions were used to construct a deep learning model based on the Mask region-based convolutional neural network (Mask R-CNN).
[Research advances in the pathological diagnosis of melanocytic tumors with artificial intelligence].
Zhonghua Bing Li Xue Za Zhi
January 2025
Department of Pathology, School of Basic Medical Sciences, Peking University Third Hospital, Beijing100191, China.
Evaluating the pathological and clinical implications of errors made by an artificial intelligence colon biopsy screening tool.
BMJ Open Gastroenterol
January 2025
Histopathology, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK.
Objective: Artificial intelligence (AI) tools for histological diagnosis offer great potential to healthcare, yet failure to understand their clinical context is delaying adoption. IGUANA (Interpretable Gland-Graphs using a Neural Aggregator) is an AI algorithm that can effectively classify colonic biopsies into normal versus abnormal categories, designed to automatically report normal cases. We performed a retrospective pathological and clinical review of the errors made by IGUANA.
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- The study investigates using artificial intelligence, specifically deep learning models (DLMs), to accurately identify radiographic landmarks before and after shoulder arthroplasty, which is typically a tedious manual process.
- A total of 240 anteroposterior radiographs were annotated, creating a dataset that trained the DLM to achieve a mean deviation of only 1.9 mm when comparing DLM-identified landmarks to those identified by surgeons.
- The DLM demonstrated promise in efficiently analyzing radiographs with minimal human error, suggesting it could significantly aid in the evaluation of shoulder surgeries while also improving the reliability of measurements related to glenohumeral relationships.
Pinpointing the integration of artificial intelligence in liver cancer immune microenvironment.
Front Immunol
January 2025
Department of Oncology, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Liver cancer remains one of the most formidable challenges in modern medicine, characterized by its high incidence and mortality rate. Emerging evidence underscores the critical roles of the immune microenvironment in tumor initiation, development, prognosis, and therapeutic responsiveness. However, the composition of the immune microenvironment of liver cancer (LC-IME) and its association with clinicopathological significance remain unelucidated.
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