Machine Learning-Based Automated Assessment of Intracorporeal Suturing in Laparoscopic Fundoplication

Automated assessment of surgical skills using artificial intelligence (AI) provides trainees with instantaneous feedback. After bimanual tool motions are captured, derived kinematic metrics are reliable predictors of performance in laparoscopic tasks. Implementing automated tool tracking requires time-intensive human annotation. We developed AI-based tool tracking using the Segment Anything Model (SAM) to eliminate the need for human annotators. Here, we describe a study evaluating the usefulness of our tool tracking model in automated assessment during a laparoscopic suturing task in the fundoplication procedure. An automated tool tracking model was applied to recorded videos of Nissen fundoplication on porcine bowel. Surgeons were grouped as novices (PGY1-2) and experts (PGY3-5, attendings). The beginning and end of each suturing step were segmented, and motions of the left and right tools were extracted. A low-pass filter with a 24 Hz cut-off frequency removed noise. Performance was assessed using supervised and unsupervised models, and an ablation study compared results. Kinematic features--RMS velocity, RMS acceleration, RMS jerk, total path length, and Bimanual Dexterity--were extracted and analyzed using Logistic Regression, Random Forest, Support Vector Classifier, and XGBoost. PCA was performed for feature reduction. For unsupervised learning, a Denoising Autoencoder (DAE) model with classifiers, such as a 1-D CNN and traditional models, was trained. Data were extracted for 28 participants (9 novices, 19 experts). Supervised learning with PCA and Random Forest achieved an accuracy of 0.795 and an F1 score of 0.778. The unsupervised 1-D CNN achieved superior results with an accuracy of 0.817 and an F1 score of 0.806, eliminating the need for kinematic feature computation. We demonstrated an AI model capable of automated performance classification, independent of human annotation.

Large Language Models for Medical OSCE Assessment: A Novel Approach to Transcript Analysis

Grading Objective Structured Clinical Examinations (OSCEs) is a time-consuming and expensive process, traditionally requiring extensive manual effort from human experts. In this study, we explore the potential of Large Language Models (LLMs) to assess skills related to medical student communication. We analyzed 2,027 video-recorded OSCE examinations from the University of Texas Southwestern Medical Center (UTSW), spanning four years (2019-2022), and several different medical cases or "stations." Specifically, our focus was on evaluating students' ability to summarize patients' medical history: we targeted the rubric item 'did the student summarize the patients' medical history?' from the communication skills rubric. After transcribing speech audio captured by OSCE videos using Whisper-v3, we studied the performance of various LLM-based approaches for grading students on this summarization task based on their examination transcripts. Using various frontier-level open-source and proprietary LLMs, we evaluated different techniques such as zero-shot chain-of-thought prompting, retrieval augmented generation, and multi-model ensemble methods. Our results show that frontier LLM models like GPT-4 achieved remarkable alignment with human graders, demonstrating a Cohen's kappa agreement of 0.88 and indicating strong potential for LLM-based OSCE grading to augment the current grading process. Open-source models also showed promising results, suggesting potential for widespread, cost-effective deployment. Further, we present a failure analysis identifying conditions where LLM grading may be less reliable in this context and recommend best practices for deploying LLMs in medical education settings.