ArticleFrontiers in medicine2026
Enhancing renal stone detection through artificial intelligence models.
Article in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objective: Traditional ultrasonic detection for kidney stones is prone to variability due to the experience and skill of the operating physician, leading to inconsistent diagnostic outcomes. Factors such as stone size, location, and surrounding tissues can further compromise accuracy. Artificial intelligence (AI), with its advanced data processing and deep learning capabilities, offers a solution by automating the analysis of medical images to improve diagnostic accuracy and efficiency. Methods: This cross-sectional study was conducted at our hospital between 01/06/2022 and 03/07/2024. After excluding 50 poor-quality ultrasound images, the final dataset included 1,130 images: 918 stone images and 212 non-stone images. At the patient level, 791 stone images were used for training/validation, and 127 stone images plus 212 non-stone images ( Results: In the independent test cohort ( Conclusions: HRNet-based AI may improve specificity and reduce false-positive findings in ultrasound-based renal stone detection. However, the moderate sensitivity indicates a substantial risk of missed stones, and the model requires further optimization before clinical adoption. This retrospective study evaluated image-level performance only and did not directly measure patient outcomes, clinical workload, diagnostic time, or treatment decisions. Therefore, claims that this approach reduces diagnostic burden or improves patient outcomes are not supported by the present data.
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