Explainable artificial intelligence prediction-based model in laparoscopic liver surgery for segments 7 and 8: an international multicenter study

Victor Lopez-Lopez, Zeniche Morise, Mariano Albadalejo-González, Concepción Gomez Gavara, Brian K.P. Goh, Ye Xin Koh, Sijberden Jasper Paul, Mohammed Abu Hilal, Kohei Mishima, Jaime Arthur Pirola Krürger, Paulo Herman, Alvaro Cerezuela, Roberto Brusadin, Takashi Kaizu, Juan Lujan, Fernando Rotellar, Kazuteru Monden, Mar Dalmau, Naoto Gotohda, Masashi KudoAkishige Kanazawa, Yutaro Kato, Hiroyuki Nitta, Satoshi Amano, Raffaele Dalla Valle, Mario Giuffrida, Masaki Ueno, Yuichiro Otsuka, Daisuke Asano, Minoru Tanabe, Osamu Itano, Takuya Minagawa, Dilmurodjon Eshmuminov, Irene Herrero, Pablo Ramírez, José A. Ruipérez-Valiente, Ricardo Robles-Campos, Go Wakabayashi

Research output: Contribution to journalArticlepeer-review

Abstract

Background: Artificial intelligence (AI) is becoming more useful as a decision-making and outcomes predictor tool. We have developed AI models to predict surgical complexity and the postoperative course in laparoscopic liver surgery for segments 7 and 8. Methods: We included patients with lesions located in segments 7 and 8 operated by minimally invasive liver surgery from an international multi-institutional database. We have employed AI models to predict surgical complexity and postoperative outcomes. Furthermore, we have applied SHapley Additive exPlanations (SHAP) to make the AI models interpretable. Finally, we analyzed the surgeries not converted to open versus those converted to open. Results: Overall, 585 patients and 22 variables were included. Multi-layer Perceptron (MLP) showed the highest performance for predicting surgery complexity and Random Forest (RF) for predicting postoperative outcomes. SHAP detected that MLP and RF gave the highest relevance to the variables “resection type” and “largest tumor size” for predicting surgery complexity and postoperative outcomes. In addition, we explored between surgeries converted to open and non-converted, finding statistically significant differences in the variables “tumor location,” “blood loss,” “complications,” and “operation time.” Conclusion: We have observed how the application of SHAP allows us to understand the predictions of AI models in surgical complexity and the postoperative outcomes of laparoscopic liver surgery in segments 7 and 8.

Original languageEnglish
JournalSurgical endoscopy
DOIs
Publication statusAccepted/In press - 2024

All Science Journal Classification (ASJC) codes

  • Surgery

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