TY - JOUR
T1 - Laparoscopic Cholecystectomy - A Proper Model Surgery for AI based Prediction of Adverse Events? Analysis of possible predictive values on the basis of the German reimbursement statistics
AU - Berlet, Maximilian
AU - Fuchtmann, Jonas
AU - Bernhard, Lukas
AU - Jell, Alissa
AU - Weber, Marie Christin
AU - Neumann, Philipp Alexander
AU - Friess, Helmut
AU - Kranzfelder, Michael
AU - Feussner, Hubertus
AU - Wilhelm, Dirk
N1 - Publisher Copyright:
© 2022 by Walter de Gruyter Berlin/Boston.
PY - 2022/7/1
Y1 - 2022/7/1
N2 - Laparoscopic cholecystectomy (LCHE) is a widely employed model for surgical instrument and phase recognition in the field of machine learning (ML), with the latter being assigned to identify critical events and to avoid complications. Although ML algorithms have been proven to be effective for this instance and in selected patients, it is questionable whether patients receiving LCHE in daily clinical routine would actually benefit from adverse event prediction by ML applications. We believe, that the statistical problem of low prevalence (PREV) of potential adverse events in an unselected population and consequential low diagnostic yield was not considered adequately in recent research. Therefore, we performed a query to the G-DRG (German Diagnosis Related Groups) database of the German Federal Statistical Office with the aim to calculate prevalence of surgical and postoperative adverse events coming along with LCHE. The results enable an estimation of positive (PPV) and negative (NPV) predictive values hypothetically achievable by ML applications aiming to predict an adverse surgical course.
AB - Laparoscopic cholecystectomy (LCHE) is a widely employed model for surgical instrument and phase recognition in the field of machine learning (ML), with the latter being assigned to identify critical events and to avoid complications. Although ML algorithms have been proven to be effective for this instance and in selected patients, it is questionable whether patients receiving LCHE in daily clinical routine would actually benefit from adverse event prediction by ML applications. We believe, that the statistical problem of low prevalence (PREV) of potential adverse events in an unselected population and consequential low diagnostic yield was not considered adequately in recent research. Therefore, we performed a query to the G-DRG (German Diagnosis Related Groups) database of the German Federal Statistical Office with the aim to calculate prevalence of surgical and postoperative adverse events coming along with LCHE. The results enable an estimation of positive (PPV) and negative (NPV) predictive values hypothetically achievable by ML applications aiming to predict an adverse surgical course.
KW - Laparoscopic cholecystectomy
KW - adverse events
KW - artificial intelligence
KW - prevalence
UR - http://www.scopus.com/inward/record.url?scp=85135553389&partnerID=8YFLogxK
U2 - 10.1515/cdbme-2022-0002
DO - 10.1515/cdbme-2022-0002
M3 - Article
AN - SCOPUS:85135553389
SN - 2364-5504
VL - 8
SP - 5
EP - 8
JO - Current Directions in Biomedical Engineering
JF - Current Directions in Biomedical Engineering
IS - 1
ER -