Artificial intelligence for body composition assessment focusing on sarcopenia
Abstract This study aimed to address the limitations of conventional methods for measuring skeletal muscle mass for sarcopenia diagnosis by introducing an artificial intelligence (AI) system for direct computed tomography (CT) analysis. The primary focus was on enhancing simplicity, reproducibility,...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-83401-8 |
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| author | Sachiyo Onishi Takamichi Kuwahara Masahiro Tajika Tsutomu Tanaka Keisaku Yamada Masahito Shimizu Yasumasa Niwa Rui Yamaguchi |
| author_facet | Sachiyo Onishi Takamichi Kuwahara Masahiro Tajika Tsutomu Tanaka Keisaku Yamada Masahito Shimizu Yasumasa Niwa Rui Yamaguchi |
| author_sort | Sachiyo Onishi |
| collection | DOAJ |
| description | Abstract This study aimed to address the limitations of conventional methods for measuring skeletal muscle mass for sarcopenia diagnosis by introducing an artificial intelligence (AI) system for direct computed tomography (CT) analysis. The primary focus was on enhancing simplicity, reproducibility, and convenience, and assessing the accuracy and speed of AI compared with conventional methods. A cohort of 3096 cases undergoing CT imaging up to the third lumbar (L3) level between 2011 and 2021 were included. Random division into preprocessing and sarcopenia cohorts was performed, with further random splits into training and validation cohorts for BMI_AI and Body_AI creation. Sarcopenia_AI utilizes the Skeletal Muscle Index (SMI), which is calculated as (total skeletal muscle area at L3)/(height)2. The SMI was conventionally measured twice, with the first as the AI label reference and the second for comparison. Agreement and diagnostic change rates were calculated. Three groups were randomly assigned and 10 images before and after L3 were collected for each case. AI models for body region detection (Deeplabv3) and sarcopenia diagnosis (EfficientNetV2-XL) were trained on a supercomputer, and their abilities and speed per image were evaluated. The conventional method showed a low agreement rate (κ coefficient) of 0.478 for the test cohort and 0.236 for the validation cohort, with diagnostic changes in 43% of cases. Conversely, the AI consistently produced identical results after two measurements. The AI demonstrated robust body region detection ability (intersection over Union (IoU) = 0.93), accurately detecting only the body region in all images. The AI for sarcopenia diagnosis exhibited high accuracy, with a sensitivity of 82.3%, specificity of 98.1%, and a positive predictive value of 89.5%. In conclusion, the reproducibility of the conventional method for sarcopenia diagnosis was low. The developed sarcopenia diagnostic AI, with its high positive predictive value and convenient diagnostic capabilities, is a promising alternative for addressing the shortcomings of conventional approaches. |
| format | Article |
| id | doaj-art-9141ef80058640d589746e5809312c7f |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-9141ef80058640d589746e5809312c7f2025-01-12T12:16:36ZengNature PortfolioScientific Reports2045-23222025-01-011511910.1038/s41598-024-83401-8Artificial intelligence for body composition assessment focusing on sarcopeniaSachiyo Onishi0Takamichi Kuwahara1Masahiro Tajika2Tsutomu Tanaka3Keisaku Yamada4Masahito Shimizu5Yasumasa Niwa6Rui Yamaguchi7Department of Endoscopy, Aichi Cancer CenterDepartment of Gastroenterology, Aichi Cancer CenterDepartment of Endoscopy, Aichi Cancer CenterDepartment of Endoscopy, Aichi Cancer CenterDepartment of Endoscopy, Aichi Cancer CenterDepartment of Gastroenterology/Internal Medicine, Gifu University School of Medicine Graduate School of MedicineDepartment of Endoscopy, Aichi Cancer CenterDivision of Cancer Systems Biology, Aichi Cancer Center Research InstituteAbstract This study aimed to address the limitations of conventional methods for measuring skeletal muscle mass for sarcopenia diagnosis by introducing an artificial intelligence (AI) system for direct computed tomography (CT) analysis. The primary focus was on enhancing simplicity, reproducibility, and convenience, and assessing the accuracy and speed of AI compared with conventional methods. A cohort of 3096 cases undergoing CT imaging up to the third lumbar (L3) level between 2011 and 2021 were included. Random division into preprocessing and sarcopenia cohorts was performed, with further random splits into training and validation cohorts for BMI_AI and Body_AI creation. Sarcopenia_AI utilizes the Skeletal Muscle Index (SMI), which is calculated as (total skeletal muscle area at L3)/(height)2. The SMI was conventionally measured twice, with the first as the AI label reference and the second for comparison. Agreement and diagnostic change rates were calculated. Three groups were randomly assigned and 10 images before and after L3 were collected for each case. AI models for body region detection (Deeplabv3) and sarcopenia diagnosis (EfficientNetV2-XL) were trained on a supercomputer, and their abilities and speed per image were evaluated. The conventional method showed a low agreement rate (κ coefficient) of 0.478 for the test cohort and 0.236 for the validation cohort, with diagnostic changes in 43% of cases. Conversely, the AI consistently produced identical results after two measurements. The AI demonstrated robust body region detection ability (intersection over Union (IoU) = 0.93), accurately detecting only the body region in all images. The AI for sarcopenia diagnosis exhibited high accuracy, with a sensitivity of 82.3%, specificity of 98.1%, and a positive predictive value of 89.5%. In conclusion, the reproducibility of the conventional method for sarcopenia diagnosis was low. The developed sarcopenia diagnostic AI, with its high positive predictive value and convenient diagnostic capabilities, is a promising alternative for addressing the shortcomings of conventional approaches.https://doi.org/10.1038/s41598-024-83401-8Skeletal muscle massArtificial intelligence (AI)Sarcopenia diagnosisBody composition assessment |
| spellingShingle | Sachiyo Onishi Takamichi Kuwahara Masahiro Tajika Tsutomu Tanaka Keisaku Yamada Masahito Shimizu Yasumasa Niwa Rui Yamaguchi Artificial intelligence for body composition assessment focusing on sarcopenia Scientific Reports Skeletal muscle mass Artificial intelligence (AI) Sarcopenia diagnosis Body composition assessment |
| title | Artificial intelligence for body composition assessment focusing on sarcopenia |
| title_full | Artificial intelligence for body composition assessment focusing on sarcopenia |
| title_fullStr | Artificial intelligence for body composition assessment focusing on sarcopenia |
| title_full_unstemmed | Artificial intelligence for body composition assessment focusing on sarcopenia |
| title_short | Artificial intelligence for body composition assessment focusing on sarcopenia |
| title_sort | artificial intelligence for body composition assessment focusing on sarcopenia |
| topic | Skeletal muscle mass Artificial intelligence (AI) Sarcopenia diagnosis Body composition assessment |
| url | https://doi.org/10.1038/s41598-024-83401-8 |
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