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

Maximilian Berlet, Jonas Fuchtmann, Lukas Bernhard, Alissa Jell, Marie Christin Weber, Philipp Alexander Neumann, Helmut Friess, Michael Kranzfelder, Hubertus Feussner, Dirk Wilhelm

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)5-8
Number of pages4
JournalCurrent Directions in Biomedical Engineering
Volume8
Issue number1
DOIs
StatePublished - 1 Jul 2022
Externally publishedYes

Keywords

  • Laparoscopic cholecystectomy
  • adverse events
  • artificial intelligence
  • prevalence

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