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Machine learning model MASKRCNN_RESNET50_FPN in image segmentation for the detection of the long head of biceps in arthroscopy images.
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6 authors.
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Abstract
ObjectiveSurgical decision-making during arthroscopic procedures often relies on the subjective visual assessment of tissue morphology, particularly the redness of the long head of the biceps tendon (LHB), which serves as an indicator for potential surgical intervention. This subjectivity introduces variability across clinicians, underscoring the need for objective, data-driven methods. The present study aimed to evaluate the feasibility of applying the MASKRCNN_RESNET50_FPN deep learning model for automated segmentation of the LHB in arthroscopic images, thereby converting subjective visual assessments into quantifiable, reproducible measurements.MethodsThis original research article is a retrospective cohort analysis of arthroscopic images obtained from 124 participants (84 male, 40 female) who underwent arthroscopic procedures between March 2015 and May 2019 at a single institution. From a comprehensive database of 130,000 arthroscopic images, 15,000 images featuring the LHB were identified, and 200 were selected for manual annotation by an experienced physician. The MASKRCNN_RESNET50_FPN model, combining a ResNet50 backbone with a Feature Pyramid Network and a mask prediction branch, was trained for 10,000 iterations using the AdamW optimizer (learning rate 1×10
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