Validation of Novel Image Processing Method for Objective Quantification of Intra-Articular Bleeding During Arthroscopic Procedures.

Visual clarity is crucial for shoulder arthroscopy, directly influencing surgical precision and outcomes. Despite advances in imaging technology, intraoperative bleeding remains a significant obstacle to optimal visibility, with subjective evaluation methods lacking consistency and standardization. This study proposes a novel image processing system to objectively quantify bleeding and assess surgical effectiveness. The system uses color recognition algorithms to calculate a bleeding score based on pixel ratios by incorporating multiple color spaces to enhance accuracy and minimize errors. Moreover, 200 three-second video clips from prior arthroscopic rotator cuff repairs were evaluated by three senior surgeons trained on the system's color metrics and scoring process. Assessments were repeated two weeks later to test intraobserver reliability. The system's scores were compared to the average score given by the surgeons. The average surgeon-assigned score was 5.10 (range: 1-9.66), while the system scored videos from 1 to 9.46, with an average of 5.08. The mean absolute error between system and surgeon scores was 0.56, with a standard deviation of 0.50, achieving agreement ranging from [0.96,0.98] with 96.7% confidence (ICC = 0.967). This system provides a standardized method to evaluate intraoperative bleeding, enabling the precise detection of blood variations and supporting advanced technologies like autonomous arthropumps to enhance arthroscopy and surgical outcomes.