Human AI collaboration for unsupervised categorization of live surgical feedback.

Formative verbal feedback during live surgery is essential for adjusting trainee behavior and accelerating skill acquisition. Despite its importance, understanding optimal feedback is challenging due to the difficulty of capturing and categorizing feedback at scale. We propose a Human-AI Collaborative Refinement Process that uses unsupervised machine learning (Topic Modeling) with human refinement to discover feedback categories from surgical transcripts.

Surgical gestures to evaluate apical dissection of robot-assisted radical prostatectomy.

Previously, our group established a surgical gesture classification system that deconstructs robotic tissue dissection into basic surgical maneuvers. Here, we evaluate gestures by correlating the metric with surgeon experience and technical skill assessment scores in the apical dissection (AD) of robotic-assisted radical prostatectomy (RARP). Additionally, we explore the association between AD performance and early continence recovery following RARP.

Genomics and Artificial Intelligence: Prostate Cancer.

Artificial intelligence (AI) is revolutionizing prostate cancer genomics research. By leveraging machine learning and deep learning algorithms, researchers can rapidly analyze vast genomic datasets to identify patterns and correlations that may be missed by traditional methods. These AI-driven insights can lead to the discovery of novel biomarkers, enhance the accuracy of diagnosis, and predict disease progression and treatment response.

Artificial Intelligence-Based Video Feedback to Improve Novice Performance on Robotic Suturing Skills: A Pilot Study.

Automated skills assessment can provide surgical trainees with objective, personalized feedback during training. Here, we measure the efficacy of artificial intelligence (AI)-based feedback on a robotic suturing task. Forty-two participants with no robotic surgical experience were randomized to a control or feedback group and video-recorded while completing two rounds (R1 and R2) of suturing tasks on a da Vinci surgical robot.

Development of a Classification System for Live Surgical Feedback.

Importance: Live feedback in the operating room is essential in surgical training. Despite the role this feedback plays in developing surgical skills, an accepted methodology to characterize the salient features of feedback has not been defined.

Objective: To quantify the intraoperative feedback provided to trainees during live surgical cases and propose a standardized deconstruction for feedback.

A Multi-institution Study on the Association of Virtual Reality Skills with Continence Recovery after Robot-assisted Radical Prostatectomy.

Background: Virtual reality (VR) simulators are increasingly being used for surgical skills training. It is unclear what skills are best improved via VR, translate to live surgical skills, and influence patient outcomes.

Objective: To assess surgeons in VR and live surgery using a suturing assessment tool and evaluate the association between technical skills and a clinical outcome.

Exploring the Use of Artificial Intelligence in the Management of Prostate Cancer.

Purpose Of Review: This review aims to explore the current state of research on the use of artificial intelligence (AI) in the management of prostate cancer. We examine the various applications of AI in prostate cancer, including image analysis, prediction of treatment outcomes, and patient stratification. Additionally, the review will evaluate the current limitations and challenges faced in the implementation of AI in prostate cancer management.

Surgical gestures as a method to quantify surgical performance and predict patient outcomes.

How well a surgery is performed impacts a patient's outcomes; however, objective quantification of performance remains an unsolved challenge. Deconstructing a procedure into discrete instrument-tissue "gestures" is a emerging way to understand surgery. To establish this paradigm in a procedure where performance is the most important factor for patient outcomes, we identify 34,323 individual gestures performed in 80 nerve-sparing robot-assisted radical prostatectomies from two international medical centers.

Using Real-time Feedback To Improve Surgical Performance on a Robotic Tissue Dissection Task.

Background: There is no standard for the feedback that an attending surgeon provides to a training surgeon, which may lead to variable outcomes in teaching cases.

Objective: To create and administer standardized feedback to medical students in an attempt to improve performance and learning.

Design Setting And Participants: A cohort of 45 medical students was recruited from a single medical school.

SCiPad: evaluating telemedicine via iPad facetime for general spinal cord injury care.

Study Design: Uncontrolled clinical pilot study.

Objectives: To assess usage, perceived impact, and satisfaction with a telemedicine program among individuals with spinal cord injury (tele-SCI).

Setting: Community-based.

Inter-eye genomic heterogeneity in bilateral retinoblastoma via aqueous humor liquid biopsy.

Germline alterations in the RB1 tumor suppressor gene predispose patients to develop retinoblastoma (RB) in both eyes. While similar treatment is given for each eye, there is often a variable therapeutic response between the eyes. Herein, we use the aqueous humor (AH) liquid biopsy to evaluate the cell-free tumor DNA (ctDNA) from each eye in a patient with bilateral RB.