All Publications

  • Using AI and computer vision to analyze technical proficiency in robotic surgery. Surgical endoscopy Yang, J. H., Goodman, E. D., Dawes, A. J., Gahagan, J. V., Esquivel, M. M., Liebert, C. A., Kin, C., Yeung, S., Gurland, B. H. 2022


    BACKGROUND: Intraoperative skills assessment is time-consuming and subjective; an efficient and objective computer vision-based approach for feedback is desired. In this work, we aim to design and validate an interpretable automated method to evaluate technical proficiency using colorectal robotic surgery videos with artificial intelligence.METHODS: 92 curated clips of peritoneal closure were characterized by both board-certified surgeons and a computer vision AI algorithm to compare the measures of surgical skill. For human ratings, six surgeons graded clips according to the GEARS assessment tool; for AI assessment, deep learning computer vision algorithms for surgical tool detection and tracking were developed and implemented.RESULTS: For the GEARS category of efficiency, we observe a positive correlation between human expert ratings of technical efficiency and AI-determined total tool movement (r=-0.72). Additionally, we show that more proficient surgeons perform closure with significantly less tool movement compared to less proficient surgeons (p<0.001). For the GEARS category of bimanual dexterity, a positive correlation between expert ratings of bimanual dexterity and the AI model's calculated measure of bimanual movement based on simultaneous tool movement (r=0.48) was also observed. On average, we also find that higher skill clips have significantly more simultaneous movement in both hands compared to lower skill clips (p<0.001).CONCLUSIONS: In this study, measurements of technical proficiency extracted from AI algorithms are shown to correlate with those given by expert surgeons. Although we target measurements of efficiency and bimanual dexterity, this work suggests that artificial intelligence through computer vision holds promise for efficiently standardizing grading of surgical technique, which may help in surgical skills training.

    View details for DOI 10.1007/s00464-022-09781-y

    View details for PubMedID 36536082

  • Nurturing diversity and inclusion in AI in Biomedicine through a virtual summer program for high school students. PLoS computational biology Oskotsky, T., Bajaj, R., Burchard, J., Cavazos, T., Chen, I., Connell, W., Eaneff, S., Grant, T., Kanungo, I., Lindquist, K., Myers-Turnbull, D., Naing, Z. Z., Tang, A., Vora, B., Wang, J., Karim, I., Swadling, C., Yang, J., Lindstaedt, B., Sirota, M. 2022; 18 (1): e1009719


    Artificial Intelligence (AI) has the power to improve our lives through a wide variety of applications, many of which fall into the healthcare space; however, a lack of diversity is contributing to limitations in how broadly AI can help people. The UCSF AI4ALL program was established in 2019 to address this issue by targeting high school students from underrepresented backgrounds in AI, giving them a chance to learn about AI with a focus on biomedicine, and promoting diversity and inclusion. In 2020, the UCSF AI4ALL three-week program was held entirely online due to the COVID-19 pandemic. Thus, students participated virtually to gain experience with AI, interact with diverse role models in AI, and learn about advancing health through AI. Specifically, they attended lectures in coding and AI, received an in-depth research experience through hands-on projects exploring COVID-19, and engaged in mentoring and personal development sessions with faculty, researchers, industry professionals, and undergraduate and graduate students, many of whom were women and from underrepresented racial and ethnic backgrounds. At the conclusion of the program, the students presented the results of their research projects at our final symposium. Comparison of pre- and post-program survey responses from students demonstrated that after the program, significantly more students were familiar with how to work with data and to evaluate and apply machine learning algorithms. There were also nominally significant increases in the students' knowing people in AI from historically underrepresented groups, feeling confident in discussing AI, and being aware of careers in AI. We found that we were able to engage young students in AI via our online training program and nurture greater diversity in AI. This work can guide AI training programs aspiring to engage and educate students entirely online, and motivate people in AI to strive towards increasing diversity and inclusion in this field.

    View details for DOI 10.1371/journal.pcbi.1009719

    View details for PubMedID 35100256