Explaining a model predicting quality of surgical practice: a first presentation to and review by clinical experts.

Abstract

Surgical Data Science (SDS) is an emerging research domain offering data-driven answers to challenges encountered by clinicians during training and practice. We previously developed a framework to assess quality of practice based on two aspects: exposure of the surgical scene (ESS) and the surgeon's profile of practice (SPP). Here, we wished to investigate the clinical relevance of the parameters learned by this model by (1) interpreting these parameters and identifying associated representative video samples and (2) presenting this information to surgeons in the form of a video-enhanced questionnaire. To our knowledge, this is the first approach in the field of SDS for laparoscopy linking the choices made by a machine learning model predicting surgical quality to clinical expertise. Spatial features and quality of practice scores extracted from labeled and segmented frames in 30 laparoscopic videos were used to predict the ESS and the SPP. The relationships between the inputs and outputs of the model were then analyzed and translated into meaningful sentences (statements, e.g., "To optimize the ESS, it is very important to correctly handle the spleen"). Representative video clips illustrating these statements were semi-automatically identified. Eleven statements and video clips were used in a survey presented to six experienced digestive surgeons to gather their opinions on the algorithmic analyses. All but one of the surgeons agreed with the proposed questionnaire overall. On average, surgeons agreed with 7/11 statements. This proof-of-concept study provides preliminary validation of our model which has a high potential for use to analyze and understand surgical practices.