Li, Y., Song, Z., Gong, Z., Huang, S., & Ge, J. (2023). Multimodal growth and development assessment model. [Journal Name Not Provided].
This paper aims to address the limitations of traditional growth and development assessment methods in children by proposing a novel multimodal model that integrates various data sources, including imaging, physiological parameters, and patient history.
The researchers developed a multimodal growth and development assessment model utilizing a hybrid architecture combining the Xception model for processing X-ray images and the Transformer model for analyzing patient case reports. The model incorporates an ICL (In-Context Learning) module to enhance its understanding of complex cases and improve diagnostic accuracy. The model was trained using the RSNA public dataset and evaluated using a synthetic dataset from Huaibei People's Hospital.
The developed model demonstrated significant improvements in accuracy and efficiency compared to traditional methods. The model achieved a loss rate of approximately 5% after five iterations during training and showed a substantial reduction in prediction error. The evaluation using the test dataset demonstrated the model's strong generalization ability and its capacity to handle a wide range of cases.
The proposed multimodal growth and development assessment model offers a more comprehensive and accurate approach to evaluating children's growth. The integration of multiple data sources and the use of advanced machine learning techniques contribute to its enhanced performance. The model has the potential to significantly impact child health monitoring and intervention strategies, particularly in addressing health inequalities and promoting healthy development globally.
This research introduces a valuable tool for improving pediatric healthcare by providing a more precise and reliable method for assessing growth and development in children. The model's ability to integrate various data modalities and its adaptability to complex cases make it a significant contribution to the field.
While the study demonstrates the model's effectiveness, it acknowledges the need for further research with larger and more diverse datasets to enhance its generalizability. Future studies could explore the model's application in different clinical settings and investigate its potential for predicting future growth trajectories and identifying early signs of growth disorders.
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by Ying Li, Zic... pada arxiv.org 10-18-2024
https://arxiv.org/pdf/2410.13647.pdfPertanyaan yang Lebih Dalam