Automated Surgical Learning and Debriefing Platform

Unmet Need

A cataract is a condition denoted by the blurring of the lens of the eye that accounts for 51% of blindness and 33% of visual impairments worldwide (see WHO). The number of people in the U.S. with cataracts is expected to double from 24.4 million in 2010 to a projected 50 million in 2050 (see NIH). Surgery is the only corrective treatment intervention for cataracts and is associated with a significantly lowered risk of death, hip fractures, or car accidents. Notably, the experience levels of surgeons play a significant role in the quality of cataract surgeries. Surgeons in their first year are nine times more likely to have high complication rates, versus surgeons in their tenth year. Each incremental year of surgical practice is associated with a 10% lower risk of adverse surgical events (see Ophthamology). Nevertheless, practicing surgeons have limited opportunities to receive critical feedback. Post-residency education is limited to sporadic one-on-one mentoring by experienced colleagues, a process that is inefficient to implement at the scale that is required to meet the needs of newly graduating surgeons. As such, there is a strong need for learning tools that accelerate improvements in cataract surgeries and, thereby, improve the quality of care for the millions of patients with cataracts.

Technology Overview

Inventors at Johns Hopkins have developed a machine learning-enabled platform called eyeLearn that allows surgeons to (1) watch back recordings of their procedure (full or in steps), (2) receive an automated, unbiased, and objective assessment of their skill for target steps, and (3) review pertinent examples with feedback on how to improve their performance. The eyeLearn platform aims to accelerate learning and skill acquisition for surgeons across the career spectrum – whether they are in training or in independent practice.

Stage of Development

Proof of concept algorithms are available that (1) automatically segment videos of cataract surgery into constituent phases for subsequent automated skill assessment and feedback (see publication) and (2) automatically model & assess surgical technical skill in capsulorhexis, a critical step in cataract surgery (see publication).

Publication

Kim, T.S., O’Brien, M., Zafar, S. et al. Objective assessment of intraoperative technical skill in capsulorhexis using videos of cataract surgery. Int J CARS 14, 1097–1105 (2019). https://doi.org/10.1007/s11548-019-01956-8