PDF(Pubmed)
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.\"
CONCLUSIONS: 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.
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.\"
CONCLUSIONS: 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.
摘要：
背景：人工智能（AI）作为决策和结果预测工具变得越来越有用。我们已经开发了AI模型来预测7和8段腹腔镜肝脏手术的手术复杂性和术后过程。
方法：我们从国际多机构数据库中纳入了通过微创肝脏手术进行的第7和第8段病变的患者。我们采用AI模型来预测手术复杂性和术后结果。此外，我们已经应用了Shapley加法扩张（SHAP）来使AI模型可解释。最后,我们分析了未转换为开放的手术与转换为开放的手术。
结果：总体而言，包括585例患者和22个变量。多层感知器（MLP）在预测手术复杂性方面表现出最高的性能，而随机森林（RF）在预测术后结果方面表现出最高的性能。SHAP检测到MLP和RF对预测手术复杂性和术后结果的变量“切除类型”和“最大肿瘤大小”的相关性最高。此外,我们探索了转换为开放和非转换的手术，发现变量“肿瘤位置”的统计学显著差异，\"\"失血,“\”并发症，\"和\"操作时间。
结论：我们已经观察到SHAP的应用如何使我们能够了解AI模型对手术复杂性的预测以及腹腔镜肝脏手术第7段和第8段的术后结果。
方法：我们从国际多机构数据库中纳入了通过微创肝脏手术进行的第7和第8段病变的患者。我们采用AI模型来预测手术复杂性和术后结果。此外，我们已经应用了Shapley加法扩张（SHAP）来使AI模型可解释。最后,我们分析了未转换为开放的手术与转换为开放的手术。
结果：总体而言，包括585例患者和22个变量。多层感知器（MLP）在预测手术复杂性方面表现出最高的性能，而随机森林（RF）在预测术后结果方面表现出最高的性能。SHAP检测到MLP和RF对预测手术复杂性和术后结果的变量“切除类型”和“最大肿瘤大小”的相关性最高。此外,我们探索了转换为开放和非转换的手术，发现变量“肿瘤位置”的统计学显著差异，\"\"失血,“\”并发症，\"和\"操作时间。
结论：我们已经观察到SHAP的应用如何使我们能够了解AI模型对手术复杂性的预测以及腹腔镜肝脏手术第7段和第8段的术后结果。
