Preserving privacy in surgical video analysis using a deep learning classifier to identify out-of-body scenes in endoscopic videos

Surgical video analysis facilitates education and research. However, video recordings of endoscopic surgeries can contain privacy-sensitive information, especially if the endoscopic camera is moved out of the body of patients and out-of-body scenes are recorded. Therefore, identification of out-of-body scenes in endoscopic videos is of major importance to preserve the privacy of patients and operating room staff. This study developed and validated a deep learning model for the identification of out-of-body images in endoscopic videos. The model was trained and evaluated on an internal dataset of 12 different types of laparoscopic and robotic surgeries and was externally validated on two independent multicentric test datasets of laparoscopic gastric bypass and cholecystectomy surgeries. Model performance was evaluated compared to human ground truth annotations measuring the receiver operating characteristic area under the curve (ROC AUC). The internal dataset consisting of 356,267 images from 48 videos and the two multicentric test datasets consisting of 54,385 and 58,349 images from 10 and 20 videos, respectively, were annotated. The model identified out-of-body images with 99.97% ROC AUC on the internal test dataset. Mean +/- standard deviation ROC AUC on the multicentric gastric bypass dataset was 99.94 +/- 0.07% and 99.71 +/- 0.40% on the multicentric cholecystectomy dataset, respectively. The model can reliably identify out-of-body images in endoscopic videos and is publicly shared. This facilitates privacy preservation in surgical video analysis

Automation of surgical skill assessment using a three-stage machine learning algorithm

Surgical skills are associated with clinical outcomes. To improve surgical skills and thereby reduce adverse outcomes, continuous surgical training and feedback is required. Currently, assessment of surgical skills is a manual and time-consuming process which is prone to subjective interpretation. This study aims to automate surgical skill assessment in laparoscopic cholecystectomy videos using machine learning algorithms. To address this, a three-stage machine learning method is proposed: first, a Convolutional Neural Network was trained to identify and localize surgical instruments. Second, motion features were extracted from the detected instrument localizations throughout time. Third, a linear regression model was trained based on the extracted motion features to predict surgical skills. This three-stage modeling approach achieved an accuracy of 87 ± 0.2% in distinguishing good versus poor surgical skill. While the technique cannot reliably quantify the degree of surgical skill yet it represents an important advance towards automation of surgical skill assessment

Growth potential of U-clip interrupted versus polypropylene running suture anastomosis in congenital cardiac surgery: intermediate term results

Although U-clip anastomoses were studied for hemodynamics and patency, their potential for unimpeded growth after congenital cardiovascular surgery has not been investigated yet. In 53 children aged 2.1+/-3.3 years operated on between March 1998 and August 2005 growth of U-clip (U) vs. polypropylene running sutured (P) anastomoses in coarctation repair (Coarc; n=26), bi-directional Glenn (BDG; n=13) and arterial switch operation (ASO; n=14) was retrospectively analysed. Coarc showed 2.39+/-4.33 vs. 3.09+/-2.24 mm of growth during the observation period (21+/-16 vs. 30+/-27 months); no growth (0 vs.16%), restenosis (14 vs. 37%) and reinterventions (14 vs. 11%) were similar (all in U vs. P, P=ns). BDG showed 3.68+/-3.43 vs. 2.50+/-2.55 mm (P=ns) of growth during 15+/-5 vs. 29+/-18 months (P=0.046); no growth (17 vs. 0%), stenosis (0 vs. 14%) and reinterventions (0%) were similar in U vs. P, respectively (P=ns). Main pulmonary artery (MPA) anastomosis in ASO showed 0.28+/-1.73 vs. 1.30+/-3.16 mm of growth during 8+/-14 vs. 28+/-28 months; no growth (60 vs. 14%), stenosis (50 vs. 63%) and reinterventions (0%) were similar (all in U vs. P, P=ns). Anastomotic growth, stenosis and reintervention rates show no difference between interrupted U-clip and polypropylene running sutured technique in Coarc repair, BDG and MPA anastomosis in ASO