Multimodal growth and development assessment model
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866910654575673344 |
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| author | Li, Ying Song, Zichen Gong, Zijie Huang, Sitan Ge, Jiewei |
| author_facet | Li, Ying Song, Zichen Gong, Zijie Huang, Sitan Ge, Jiewei |
| contents | With the development of social economy and the improvement of people's attention to health, the growth and development of children and adolescents has become an important indicator to measure the level of national health. Therefore, accurate and timely assessment of children's growth and development has become increasingly important. At the same time, global health inequalities, especially child malnutrition and stunting in developing countries, urgently require effective assessment tools to monitor and intervene. In recent years, the rapid development of technologies such as big data, artificial intelligence, and cloud computing, and the cross-integration of multiple disciplines such as biomedicine, statistics, and computer science have promoted the rapid development of large-scale models for growth and development assessment. However, there are still problems such as too single evaluation factors, inaccurate diagnostic results, and inability to give accurate and reasonable recommendations. The multi-modal growth and development assessment model uses the public data set of RSNA ( North American College of Radiology ) as the training set, and the data set of the Department of Pediatrics of Huaibei People's Hospital as the open source test set. The embedded ICL module enables the model to quickly adapt and identify the tasks that need to be done to ensure that under the premise of considering multiple evaluation factors, accurate diagnosis results and reasonable medical recommendations are given, so as to provide solutions to the above problems and promote the development of the medical field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_13647 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multimodal growth and development assessment model Li, Ying Song, Zichen Gong, Zijie Huang, Sitan Ge, Jiewei Computational Engineering, Finance, and Science Multimedia With the development of social economy and the improvement of people's attention to health, the growth and development of children and adolescents has become an important indicator to measure the level of national health. Therefore, accurate and timely assessment of children's growth and development has become increasingly important. At the same time, global health inequalities, especially child malnutrition and stunting in developing countries, urgently require effective assessment tools to monitor and intervene. In recent years, the rapid development of technologies such as big data, artificial intelligence, and cloud computing, and the cross-integration of multiple disciplines such as biomedicine, statistics, and computer science have promoted the rapid development of large-scale models for growth and development assessment. However, there are still problems such as too single evaluation factors, inaccurate diagnostic results, and inability to give accurate and reasonable recommendations. The multi-modal growth and development assessment model uses the public data set of RSNA ( North American College of Radiology ) as the training set, and the data set of the Department of Pediatrics of Huaibei People's Hospital as the open source test set. The embedded ICL module enables the model to quickly adapt and identify the tasks that need to be done to ensure that under the premise of considering multiple evaluation factors, accurate diagnosis results and reasonable medical recommendations are given, so as to provide solutions to the above problems and promote the development of the medical field. |
| title | Multimodal growth and development assessment model |
| topic | Computational Engineering, Finance, and Science Multimedia |
| url | https://arxiv.org/abs/2410.13647 |