Research Opportunities

Faculty Research Advisors: Swaroop Vedula, Vishal Patel, Shameema Sikder

Abstract: This research explores a data-driven approach for assessing surgical skill in operating room videos of cataract surgery, focusing on two critical steps: the main incision and capsulorhexis. Surgical skill assessment and feedback have traditionally been subjective and inconsistent, heavily dependent on human raters. On the other hand, AI enables objective and consistent assessment and feedback, offering a promising solution to these limitations. To harness this potential, we developed an AI-driven assessment pipeline using a Video Masked Autoencoder (VideoMAE) model to extract temporally and spatially rich features from surgical videos. The model was pre-trained on full-length cataract surgery videos and tested on curated datasets with ground truth skill ratings provided by an expert ophthalmologist using the International Council of Ophthalmology’s Ophthalmology Surgical Competency Assessment Rubric criteria. Pre-training provided superior spatial-temporal representations for skill assessment, outperforming convolutional neural network-based architectures like CNN-Long Short Term Memory and CNN-Vision Transformer. Specifically, pre-training significantly enhanced the specificity and sensitivity of the assessment for both the main incision and capsulorhexis, surpassing the performance of previous methods. We compared the performance of the VideoMAE-based transformer model against baseline architectures using multiple random splits and standard evaluation metrics such as accuracy, area under the curve, sensitivity, and specificity. For the main incision, the model achieved an accuracy of 0.76, sensitivity of 0.73, and specificity of 0.80. For capsulorhexis, it achieved an accuracy of 0.71, sensitivity of 0.85, and specificity of 0.90. Importantly, while the AI models were validated using cross-validation, further validation on new datasets is needed to confirm their generalizability. These findings highlight that VideoMAE enables an objective, automated evaluation of critical surgical steps in cataract surgery, improving assessment accuracy and consistency. Its ability to capture subtle temporal transitions and provide fine-grained spatial analysis makes it more effective than conventional frame-level CNN features. These results suggest a promising future for transformer-based video models in automating surgical training and delivering objective, actionable feedback to surgeons.

About Subhasri: Subhasri Vijay is a master’s student of computer science at the Johns Hopkins University, where she also earned her undergraduate degree in computer science with minors in computational medicine and entrepreneurship and management. She has a strong foundation in AI, full-stack software development, data analysis, and problem-solving, with a deep passion for using technology to drive real-world impact. As a research assistant at the Wilmer Eye Institute and the Malone Center for Engineering in Healthcare, Vijay collaborates with ophthalmologists and engineers to develop AI solutions for surgical skill assessment that leverages deep learning and video analysis to enhance surgical training and patient outcomes. Her work has been presented at leading conferences including the International Symposium on Biomedical Imaging, the Annual Meeting of the Association of University Professors of Ophthalmology, and the Medical Imaging with Deep Learning conference, and has been recognized through internal honors such as the department’s Masson Fellowship. Vijay has served as the president of the Software Engineering Club and has helped to build an inclusive and vibrant software engineering community here at Hopkins. She currently serves as head course assistant for Data Structures, mentoring her peers and supporting the course’s teaching staff. Whether advancing AI in health care, improving emergency response through her app RescueReady, or empowering the next generation of engineers, Vijay brings a hands-on, mission-driven approach to innovation.