Artificial intelligence‐based body composition analysis using computed tomography images predicts both prevalence and incidence of diabetes mellitus
ABSTRACT Aim/Introduction We assess the efficacy of artificial intelligence (AI)‐based, fully automated, volumetric body composition metrics in predicting the risk of diabetes. Materials and Methods This was a cross‐sectional and 10‐year retrospective longitudinal study. The cross‐sectional analysis...
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| Format: | Article |
| Language: | English |
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Wiley
2025-02-01
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| Series: | Journal of Diabetes Investigation |
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| Online Access: | https://doi.org/10.1111/jdi.14365 |
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| author | Yoo Hyung Kim Ji Won Yoon Bon Hyang Lee Jeong Hee Yoon Hun Jee Choe Tae Jung Oh Jeong Min Lee Young Min Cho |
| author_facet | Yoo Hyung Kim Ji Won Yoon Bon Hyang Lee Jeong Hee Yoon Hun Jee Choe Tae Jung Oh Jeong Min Lee Young Min Cho |
| author_sort | Yoo Hyung Kim |
| collection | DOAJ |
| description | ABSTRACT Aim/Introduction We assess the efficacy of artificial intelligence (AI)‐based, fully automated, volumetric body composition metrics in predicting the risk of diabetes. Materials and Methods This was a cross‐sectional and 10‐year retrospective longitudinal study. The cross‐sectional analysis included health check‐up data of 15,330 subjects with abdominal computed tomography (CT) images between January 1, 2011, and September 30, 2012. Of these, 10,570 subjects with available follow‐up data were included in the longitudinal analyses. The volume of each body segment included in the abdominal CT images was measured using AI‐based image analysis software. Results Visceral fat (VF) proportion and VF/subcutaneous fat (SF) ratio increased with age, and both strongly predicted the presence and risk of developing diabetes. Optimal cut‐offs for VF proportion were 24% for men and 16% for women, while VF/SF ratio values were 1.2 for men and 0.5 for women. The subjects with higher VF/SF ratio and VF proportion were associated with a greater risk of having diabetes (adjusted OR 2.0 [95% CI 1.7–2.4] in men; 2.9 [2.2–3.9] in women). In subjects with normal glucose tolerance, higher VF proportion and VF/SF ratio were associated with higher risk of developing prediabetes or diabetes (adjusted HR 1.3 [95% CI 1.1–1.4] in men; 1.4 [1.2–1.7] in women). These trends were consistently observed across each specified cut‐off value. Conclusions AI‐based volumetric analysis of abdominal CT images can be useful in obtaining body composition data and predicting the risk of diabetes. |
| format | Article |
| id | doaj-art-9b8efdffea0848478e28e7d4e91bd145 |
| institution | Kabale University |
| issn | 2040-1116 2040-1124 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Diabetes Investigation |
| spelling | doaj-art-9b8efdffea0848478e28e7d4e91bd1452025-02-01T10:02:01ZengWileyJournal of Diabetes Investigation2040-11162040-11242025-02-0116227228410.1111/jdi.14365Artificial intelligence‐based body composition analysis using computed tomography images predicts both prevalence and incidence of diabetes mellitusYoo Hyung Kim0Ji Won Yoon1Bon Hyang Lee2Jeong Hee Yoon3Hun Jee Choe4Tae Jung Oh5Jeong Min Lee6Young Min Cho7Department of Internal Medicine Seoul National University Hospital Seoul KoreaDepartment of Internal Medicine Seoul National University Hospital Healthcare System Gangnam Center Seoul KoreaDepartment of Internal Medicine Seoul National University Hospital Seoul KoreaDepartment of Radiology Seoul National University College of Medicine Seoul KoreaDepartment of Internal Medicine Seoul National University Hospital Seoul KoreaDepartment of Internal Medicine Seoul National University Bundang Hospital Seongnam KoreaDepartment of Radiology Seoul National University College of Medicine Seoul KoreaDepartment of Internal Medicine Seoul National University Hospital Seoul KoreaABSTRACT Aim/Introduction We assess the efficacy of artificial intelligence (AI)‐based, fully automated, volumetric body composition metrics in predicting the risk of diabetes. Materials and Methods This was a cross‐sectional and 10‐year retrospective longitudinal study. The cross‐sectional analysis included health check‐up data of 15,330 subjects with abdominal computed tomography (CT) images between January 1, 2011, and September 30, 2012. Of these, 10,570 subjects with available follow‐up data were included in the longitudinal analyses. The volume of each body segment included in the abdominal CT images was measured using AI‐based image analysis software. Results Visceral fat (VF) proportion and VF/subcutaneous fat (SF) ratio increased with age, and both strongly predicted the presence and risk of developing diabetes. Optimal cut‐offs for VF proportion were 24% for men and 16% for women, while VF/SF ratio values were 1.2 for men and 0.5 for women. The subjects with higher VF/SF ratio and VF proportion were associated with a greater risk of having diabetes (adjusted OR 2.0 [95% CI 1.7–2.4] in men; 2.9 [2.2–3.9] in women). In subjects with normal glucose tolerance, higher VF proportion and VF/SF ratio were associated with higher risk of developing prediabetes or diabetes (adjusted HR 1.3 [95% CI 1.1–1.4] in men; 1.4 [1.2–1.7] in women). These trends were consistently observed across each specified cut‐off value. Conclusions AI‐based volumetric analysis of abdominal CT images can be useful in obtaining body composition data and predicting the risk of diabetes.https://doi.org/10.1111/jdi.14365Artificial intelligenceBody compositionDiabetes mellitus |
| spellingShingle | Yoo Hyung Kim Ji Won Yoon Bon Hyang Lee Jeong Hee Yoon Hun Jee Choe Tae Jung Oh Jeong Min Lee Young Min Cho Artificial intelligence‐based body composition analysis using computed tomography images predicts both prevalence and incidence of diabetes mellitus Journal of Diabetes Investigation Artificial intelligence Body composition Diabetes mellitus |
| title | Artificial intelligence‐based body composition analysis using computed tomography images predicts both prevalence and incidence of diabetes mellitus |
| title_full | Artificial intelligence‐based body composition analysis using computed tomography images predicts both prevalence and incidence of diabetes mellitus |
| title_fullStr | Artificial intelligence‐based body composition analysis using computed tomography images predicts both prevalence and incidence of diabetes mellitus |
| title_full_unstemmed | Artificial intelligence‐based body composition analysis using computed tomography images predicts both prevalence and incidence of diabetes mellitus |
| title_short | Artificial intelligence‐based body composition analysis using computed tomography images predicts both prevalence and incidence of diabetes mellitus |
| title_sort | artificial intelligence based body composition analysis using computed tomography images predicts both prevalence and incidence of diabetes mellitus |
| topic | Artificial intelligence Body composition Diabetes mellitus |
| url | https://doi.org/10.1111/jdi.14365 |
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