The impact of multi-modality fusion and deep learning on adult age estimation based on bone mineral density.

IF 2.2 3区 医学 Q1 MEDICINE, LEGAL
Yongjie Cao, Ji Zhang, Yonggang Ma, Suhua Zhang, Chengtao Li, Shiquan Liu, Feng Chen, Ping Huang
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引用次数: 0

Abstract

Introduction: Age estimation, especially in adults, presents substantial challenges in different contexts ranging from forensic to clinical applications. Bone mineral density (BMD), with its distinct age-related variations, has emerged as a critical marker in this domain. This study aims to enhance chronological age estimation accuracy using deep learning (DL) incorporating a multi-modality fusion strategy based on BMD.

Methods: We conducted a retrospective analysis of 4296 CT scans from a Chinese population, covering August 2015 to November 2022, encompassing lumbar, femur, and pubis modalities. Our DL approach, integrating multi-modality fusion, was applied to predict chronological age automatically. The model's performance was evaluated using an internal real-world clinical cohort of 644 scans (December 2022 to May 2023) and an external cadaver validation cohort of 351 scans.

Results: In single-modality assessments, the lumbar modality excelled. However, multi-modality models demonstrated superior performance, evidenced by lower mean absolute errors (MAEs) and higher Pearson's R² values. The optimal multi-modality model exhibited outstanding R² values of 0.89 overall, 0.88 in females, 0.90 in males, with the MAEs of 4.05 overall, 3.69 in females, 4.33 in males in the internal validation cohort. In the external cadaver validation, the model maintained favourable R² values (0.84 overall, 0.89 in females, 0.82 in males) and MAEs (5.01 overall, 4.71 in females, 5.09 in males), highlighting its generalizability across diverse scenarios.

Conclusion: The integration of multi-modalities fusion with DL significantly refines the accuracy of adult age estimation based on BMD. The AI-based system that effectively combines multi-modalities BMD data, presenting a robust and innovative tool for accurate AAE, poised to significantly improve both geriatric diagnostics and forensic investigations.

多模态融合和深度学习对基于骨密度的成人年龄估计的影响。
引言:年龄估计,特别是在成人中,在从法医到临床应用的不同背景下提出了实质性的挑战。骨密度(BMD)具有明显的年龄相关变化,已成为该领域的关键标志。本研究旨在利用深度学习(DL)结合基于骨密度的多模态融合策略来提高实足年龄估计的准确性。方法:我们对2015年8月至2022年11月来自中国人群的4296次CT扫描进行了回顾性分析,包括腰椎、股骨和耻骨模式。我们的深度学习方法,整合了多模态融合,被用于自动预测实足年龄。该模型的性能是通过644次内部临床队列扫描(2022年12月至2023年5月)和351次外部尸体验证队列扫描来评估的。结果:在单模态评估中,腰椎模态表现较好。然而,多模态模型表现出更优越的性能,证明了更低的平均绝对误差(MAEs)和更高的Pearson’s R²值。在内部验证队列中,最优多模态模型的总体R²值为0.89,女性为0.88,男性为0.90,MAEs为4.05,女性为3.69,男性为4.33。在外部尸体验证中,该模型保持了良好的R²值(总体为0.84,女性为0.89,男性为0.82)和MAEs值(总体为5.01,女性为4.71,男性为5.09),突出了其在不同情况下的通用性。结论:多模式融合与DL的结合显著提高了基于BMD的成人年龄估计的准确性。基于人工智能的系统有效地结合了多模式BMD数据,为准确的AAE提供了一个强大而创新的工具,有望显著改善老年诊断和法医调查。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
5.80
自引率
9.50%
发文量
165
审稿时长
1 months
期刊介绍: The International Journal of Legal Medicine aims to improve the scientific resources used in the elucidation of crime and related forensic applications at a high level of evidential proof. The journal offers review articles tracing development in specific areas, with up-to-date analysis; original articles discussing significant recent research results; case reports describing interesting and exceptional examples; population data; letters to the editors; and technical notes, which appear in a section originally created for rapid publication of data in the dynamic field of DNA analysis.
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