Predictive and Explainable Artificial Intelligence for Weight Loss After Sleeve Gastrectomy: Insights from Wide and Deep Learning with Medical Image and Non-Image Data
There has been no feasible approach for predicting weight loss after bariatric surgery. This study develops wide and deep learning (WAD), a predictive and explainable artificial intelligence for weight loss after sleeve gastrectomy with medical image and non-image data, such as electronic medical re...
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MDPI AG
2025-02-01
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| author | Jaechan Park Sungsoo Park Kwang-Sig Lee Yeongkeun Kwon |
| author_facet | Jaechan Park Sungsoo Park Kwang-Sig Lee Yeongkeun Kwon |
| author_sort | Jaechan Park |
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| description | There has been no feasible approach for predicting weight loss after bariatric surgery. This study develops wide and deep learning (WAD), a predictive and explainable artificial intelligence for weight loss after sleeve gastrectomy with medical image and non-image data, such as electronic medical records (EMRs). Prospective data came from 42 patients with sleeve gastrectomy at a university hospital. They were followed for one year after surgery. The dependent variable consisted of three categories: minimal, moderate, and significant change groups, classified based on postoperative percentage total weight loss (%TWL) in body mass index. A pair of 100 images and their non-image data came from each patient, with 4200 pairs from 42 patients in total. A WAD model was trained and tested with 3200 and 1000 pairs, respectively. Here, the WAD model combined a convolutional neural network (CNN) for image data and a linear layer for non-image data (EMR). The study population included 42 patients, with a mean age of 36.6 years (standard deviation SD 11.0) and a female proportion of 58% (26/45). On average, %TWL was 19.1 (SD 2.8), 27.3 (SD 2.2), and 35.1 (SD 4.7) for the minimal, moderate, and significant change groups, respectively. The corresponding accuracy outcomes were 61%, 100%, and 75% for the minimal, moderate, and significant change groups (average 71%). When the minimal and moderate change groups were combined, the accuracy was 100% for the combined group and 75% for the significant change group, with an overall average accuracy of 88%. Baseline HOMA2-B, insulin, and vitamin B12 were major predictors of %TWL. The optimal region of interest for predicting %TWL was found to be the entire cross-section above the diaphragm. In conclusion, WAD is an effective predictive and explainable artificial intelligence for weight loss following sleeve gastrectomy with image and non-image data. The most important predictors of postoperative weight loss were identified as baseline HOMA2-B, insulin, and vitamin B12 levels, while the key region of interest (ROI) in abdominal CT imaging was the entire cross-section located above the diaphragm. |
| format | Article |
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| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-74ab49e67bc3425699f414abb3e98e3d2025-08-20T02:04:34ZengMDPI AGApplied Sciences2076-34172025-02-01155245710.3390/app15052457Predictive and Explainable Artificial Intelligence for Weight Loss After Sleeve Gastrectomy: Insights from Wide and Deep Learning with Medical Image and Non-Image DataJaechan Park0Sungsoo Park1Kwang-Sig Lee2Yeongkeun Kwon3Department of Foregut Surgery, Korea University Anam Hospital, Seoul 02841, Republic of KoreaDepartment of Foregut Surgery, Korea University Anam Hospital, Seoul 02841, Republic of KoreaAI Center, Korea University Anam Hospital, Seoul 02841, Republic of KoreaDepartment of Foregut Surgery, Korea University Anam Hospital, Seoul 02841, Republic of KoreaThere has been no feasible approach for predicting weight loss after bariatric surgery. This study develops wide and deep learning (WAD), a predictive and explainable artificial intelligence for weight loss after sleeve gastrectomy with medical image and non-image data, such as electronic medical records (EMRs). Prospective data came from 42 patients with sleeve gastrectomy at a university hospital. They were followed for one year after surgery. The dependent variable consisted of three categories: minimal, moderate, and significant change groups, classified based on postoperative percentage total weight loss (%TWL) in body mass index. A pair of 100 images and their non-image data came from each patient, with 4200 pairs from 42 patients in total. A WAD model was trained and tested with 3200 and 1000 pairs, respectively. Here, the WAD model combined a convolutional neural network (CNN) for image data and a linear layer for non-image data (EMR). The study population included 42 patients, with a mean age of 36.6 years (standard deviation SD 11.0) and a female proportion of 58% (26/45). On average, %TWL was 19.1 (SD 2.8), 27.3 (SD 2.2), and 35.1 (SD 4.7) for the minimal, moderate, and significant change groups, respectively. The corresponding accuracy outcomes were 61%, 100%, and 75% for the minimal, moderate, and significant change groups (average 71%). When the minimal and moderate change groups were combined, the accuracy was 100% for the combined group and 75% for the significant change group, with an overall average accuracy of 88%. Baseline HOMA2-B, insulin, and vitamin B12 were major predictors of %TWL. The optimal region of interest for predicting %TWL was found to be the entire cross-section above the diaphragm. In conclusion, WAD is an effective predictive and explainable artificial intelligence for weight loss following sleeve gastrectomy with image and non-image data. The most important predictors of postoperative weight loss were identified as baseline HOMA2-B, insulin, and vitamin B12 levels, while the key region of interest (ROI) in abdominal CT imaging was the entire cross-section located above the diaphragm.https://www.mdpi.com/2076-3417/15/5/2457body mass indexwide and deep learningconvolutional neural network |
| spellingShingle | Jaechan Park Sungsoo Park Kwang-Sig Lee Yeongkeun Kwon Predictive and Explainable Artificial Intelligence for Weight Loss After Sleeve Gastrectomy: Insights from Wide and Deep Learning with Medical Image and Non-Image Data Applied Sciences body mass index wide and deep learning convolutional neural network |
| title | Predictive and Explainable Artificial Intelligence for Weight Loss After Sleeve Gastrectomy: Insights from Wide and Deep Learning with Medical Image and Non-Image Data |
| title_full | Predictive and Explainable Artificial Intelligence for Weight Loss After Sleeve Gastrectomy: Insights from Wide and Deep Learning with Medical Image and Non-Image Data |
| title_fullStr | Predictive and Explainable Artificial Intelligence for Weight Loss After Sleeve Gastrectomy: Insights from Wide and Deep Learning with Medical Image and Non-Image Data |
| title_full_unstemmed | Predictive and Explainable Artificial Intelligence for Weight Loss After Sleeve Gastrectomy: Insights from Wide and Deep Learning with Medical Image and Non-Image Data |
| title_short | Predictive and Explainable Artificial Intelligence for Weight Loss After Sleeve Gastrectomy: Insights from Wide and Deep Learning with Medical Image and Non-Image Data |
| title_sort | predictive and explainable artificial intelligence for weight loss after sleeve gastrectomy insights from wide and deep learning with medical image and non image data |
| topic | body mass index wide and deep learning convolutional neural network |
| url | https://www.mdpi.com/2076-3417/15/5/2457 |
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