José Fernando Trebolle , Jorge Solano Murillo , Jesús Lobo Cobo , Carmen Pellicer Lostao , Mónica Valero Sabater , Gabriel Tirado Anglés , Irene Cantarero Carmona , María José Luesma Bartolomé
{"title":"利用人工智能技术开发和验证用于减肥手术的小肠总长度预测算法","authors":"José Fernando Trebolle , Jorge Solano Murillo , Jesús Lobo Cobo , Carmen Pellicer Lostao , Mónica Valero Sabater , Gabriel Tirado Anglés , Irene Cantarero Carmona , María José Luesma Bartolomé","doi":"10.1016/j.ciresp.2025.800124","DOIUrl":null,"url":null,"abstract":"<div><h3>Objective</h3><div>To develop a predictive model of the total length of the small intestine to be applied in bariatric surgery, allowing for the individualization of surgery for each patient.</div></div><div><h3>Methods</h3><div>Two Excel® tables were generated from a FileMaker file. Python was used through a Notebook format in Google™ Collaborator. The methodology included data transformation and scaling (MinMaxScaler), clustering (unsupervised machine learning with KMeans), data interpolation (oversampling machine learning technique SMOTE), modeling (PyCaret model - XGBoost), and validation.</div></div><div><h3>Results</h3><div>The study sample included 1090 cases. Three clusters were obtained to categorize the dataset: low, medium, and high length. The algorithm detected patients in cluster c0 with 62% accuracy and 74% sensitivity, in cluster c1 with 63% accuracy and 50% sensitivity, and in cluster c2 with 86% accuracy and 87% sensitivity. Validation was conducted with a new sample of 54 cases, showing results of 50% accuracy and 42% sensitivity for cluster c0, 58% accuracy and 61% sensitivity for cluster c1, and 30% accuracy and 43% sensitivity for cluster c2.</div></div><div><h3>Conclusions</h3><div>The development of a predictive algorithm for estimating the total length of the small intestine using clustering and machine learning techniques, along with XGBoost classification, is feasible, applicable, and potentially improvable with more data, both in terms of patient numbers and variables to consider.</div></div>","PeriodicalId":50690,"journal":{"name":"Cirugia Espanola","volume":"103 7","pages":"Article 800124"},"PeriodicalIF":1.3000,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Desarrollo y validación de algoritmo predictivo de la longitud total del intestino delgado con técnicas de inteligencia artificial para su aplicación en cirugía bariátrica\",\"authors\":\"José Fernando Trebolle , Jorge Solano Murillo , Jesús Lobo Cobo , Carmen Pellicer Lostao , Mónica Valero Sabater , Gabriel Tirado Anglés , Irene Cantarero Carmona , María José Luesma Bartolomé\",\"doi\":\"10.1016/j.ciresp.2025.800124\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Objective</h3><div>To develop a predictive model of the total length of the small intestine to be applied in bariatric surgery, allowing for the individualization of surgery for each patient.</div></div><div><h3>Methods</h3><div>Two Excel® tables were generated from a FileMaker file. Python was used through a Notebook format in Google™ Collaborator. The methodology included data transformation and scaling (MinMaxScaler), clustering (unsupervised machine learning with KMeans), data interpolation (oversampling machine learning technique SMOTE), modeling (PyCaret model - XGBoost), and validation.</div></div><div><h3>Results</h3><div>The study sample included 1090 cases. Three clusters were obtained to categorize the dataset: low, medium, and high length. The algorithm detected patients in cluster c0 with 62% accuracy and 74% sensitivity, in cluster c1 with 63% accuracy and 50% sensitivity, and in cluster c2 with 86% accuracy and 87% sensitivity. Validation was conducted with a new sample of 54 cases, showing results of 50% accuracy and 42% sensitivity for cluster c0, 58% accuracy and 61% sensitivity for cluster c1, and 30% accuracy and 43% sensitivity for cluster c2.</div></div><div><h3>Conclusions</h3><div>The development of a predictive algorithm for estimating the total length of the small intestine using clustering and machine learning techniques, along with XGBoost classification, is feasible, applicable, and potentially improvable with more data, both in terms of patient numbers and variables to consider.</div></div>\",\"PeriodicalId\":50690,\"journal\":{\"name\":\"Cirugia Espanola\",\"volume\":\"103 7\",\"pages\":\"Article 800124\"},\"PeriodicalIF\":1.3000,\"publicationDate\":\"2025-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cirugia Espanola\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0009739X2501471X\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"SURGERY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cirugia Espanola","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0009739X2501471X","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"SURGERY","Score":null,"Total":0}
Desarrollo y validación de algoritmo predictivo de la longitud total del intestino delgado con técnicas de inteligencia artificial para su aplicación en cirugía bariátrica
Objective
To develop a predictive model of the total length of the small intestine to be applied in bariatric surgery, allowing for the individualization of surgery for each patient.
Methods
Two Excel® tables were generated from a FileMaker file. Python was used through a Notebook format in Google™ Collaborator. The methodology included data transformation and scaling (MinMaxScaler), clustering (unsupervised machine learning with KMeans), data interpolation (oversampling machine learning technique SMOTE), modeling (PyCaret model - XGBoost), and validation.
Results
The study sample included 1090 cases. Three clusters were obtained to categorize the dataset: low, medium, and high length. The algorithm detected patients in cluster c0 with 62% accuracy and 74% sensitivity, in cluster c1 with 63% accuracy and 50% sensitivity, and in cluster c2 with 86% accuracy and 87% sensitivity. Validation was conducted with a new sample of 54 cases, showing results of 50% accuracy and 42% sensitivity for cluster c0, 58% accuracy and 61% sensitivity for cluster c1, and 30% accuracy and 43% sensitivity for cluster c2.
Conclusions
The development of a predictive algorithm for estimating the total length of the small intestine using clustering and machine learning techniques, along with XGBoost classification, is feasible, applicable, and potentially improvable with more data, both in terms of patient numbers and variables to consider.
期刊介绍:
Cirugía Española, an official body of the Asociación Española de Cirujanos (Spanish Association of Surgeons), will consider original articles, reviews, editorials, special articles, scientific letters, letters to the editor, and medical images for publication; all of these will be submitted to an anonymous external peer review process. There is also the possibility of accepting book reviews of recent publications related to General and Digestive Surgery.