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Perspective for Future Medicine: Multidisciplinary Computational Anatomy-Based Medicine with Artificial Intelligence

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Date 2022 Oct 26
PMID 36285135
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Abstract

Multidisciplinary computational anatomy (MCA) is a new frontier of science that provides a mathematical analysis basis for the comprehensive and useful understanding of "dynamic living human anatomy." It defines a new mathematical modeling method for the early detection and highly intelligent diagnosis and treatment of incurable or intractable diseases. The MCA is a method of scientific research on innovative areas based on the medical images that are integrated with the information related to: (1) the spatial axis, extending from a cell size to an organ size; (2) the time series axis, extending from an embryo to post mortem body; (3) the functional axis on physiology or metabolism which is reflected in a variety of medical image modalities; and (4) the pathological axis, extending from a healthy physical condition to a diseased condition. It aims to integrate multiple prediction models such as multiscale prediction model, temporal prediction model, anatomy function prediction model, and anatomy-pathology prediction model. Artificial intelligence has been introduced to accelerate the calculation of statistic mathematical analysis. The future perspective is expected to promote the development of human resources as well as a new MCA-based scientific interdisciplinary field composed of mathematical statistics, information sciences, computing data science, robotics, and biomedical engineering and clinical applications. The MCA-based medicine might be one of the solutions to overcome the difficulties in the current medicine.

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References
1.
Giffard-Roisin S, Jackson T, Fovargue L, Lee J, Delingette H, Razavi R . Noninvasive Personalization of a Cardiac Electrophysiology Model From Body Surface Potential Mapping. IEEE Trans Biomed Eng. 2017; 64(9):2206-2218. DOI: 10.1109/TBME.2016.2629849. View

2.
Maier-Hein L, Vedula S, Speidel S, Navab N, Kikinis R, Park A . Surgical data science for next-generation interventions. Nat Biomed Eng. 2019; 1(9):691-696. DOI: 10.1038/s41551-017-0132-7. View

3.
Matsuzaki T, Oda M, Kitasaka T, Hayashi Y, Misawa K, Mori K . Automated anatomical labeling of abdominal arteries and hepatic portal system extracted from abdominal CT volumes. Med Image Anal. 2014; 20(1):152-61. DOI: 10.1016/j.media.2014.11.002. View

4.
Yokota F, Otake Y, Takao M, Ogawa T, Okada T, Sugano N . Automated muscle segmentation from CT images of the hip and thigh using a hierarchical multi-atlas method. Int J Comput Assist Radiol Surg. 2018; 13(7):977-986. DOI: 10.1007/s11548-018-1758-y. View

5.
Mori K . From macro-scale to micro-scale computational anatomy: a perspective on the next 20 years. Med Image Anal. 2016; 33:159-164. DOI: 10.1016/j.media.2016.06.034. View