Multicentric validation of EndoDigest: a computer vision platform for video documentation of the critical view of safety in laparoscopic cholecystectomy

BACKGROUND

A computer vision (CV) platform named EndoDigest was recently developed to facilitate the use of surgical videos. Specifically, EndoDigest automatically provides short video clips to effectively document the critical view of safety (CVS) in laparoscopic cholecystectomy (LC). The aim of the present study is to validate EndoDigest on a multicentric dataset of LC videos.

METHODS

LC videos from 4 centers were manually annotated with the time of the cystic duct division and an assessment of CVS criteria. Incomplete recordings, bailout procedures and procedures with an intraoperative cholangiogram were excluded. EndoDigest leveraged predictions of deep learning models for workflow analysis in a rule-based inference system designed to estimate the time of the cystic duct division. Performance was assessed by computing the error in estimating the manually annotated time of the cystic duct division. To provide concise video documentation of CVS, EndoDigest extracted video clips showing the 2 min preceding and the 30 s following the predicted cystic duct division. The relevance of the documentation was evaluated by assessing CVS in automatically extracted 2.5-min-long video clips.

RESULTS

144 of the 174 LC videos from 4 centers were analyzed. EndoDigest located the time of the cystic duct division with a mean error of 124.0 ± 270.6 s despite the use of fluorescent cholangiography in 27 procedures and great variations in surgical workflows across centers. The surgical evaluation found that 108 (75.0%) of the automatically extracted short video clips documented CVS effectively.

CONCLUSIONS

EndoDigest was robust enough to reliably locate the time of the cystic duct division and efficiently video document CVS despite the highly variable workflows. Training specifically on data from each center could improve results; however, this multicentric validation shows the potential for clinical translation of this surgical data science tool to efficiently document surgical safety.

Development and comparative evaluation of SARS-CoV-2 S-RBD and N based ELISA tests in various African endemic settings

Management of the COVID-19 pandemic relies on molecular diagnostic methods supported by serological tools. Herein, we developed S-RBD- and N- based ELISA assays useful for infection rate surveillance as well as the follow-up of acquired protective immunity against SARS-CoV-2. ELISA assays were optimized using COVID-19 Tunisian patients' sera and prepandemic controls. Assays were further validated in 3 African countries with variable endemic settings. The receiver operating curve was used to evaluate the assay performances. The N- and S-RBD-based ELISA assays performances, in Tunisia, were very high (AUC: 0.966 and 0.98, respectively, p < 0.0001). Cross-validation analysis showed similar performances in different settings. Cross-reactivity, with malaria infection, against viral antigens, was noticed. In head-to-head comparisons with different commercial assays, the developed assays showed high agreement. This study demonstrates, the added value of the developed serological assays in low-income countries, particularly in ethnically diverse populations with variable exposure to local endemic infectious diseases.