Department of Medical AI Research

MIRAI

Medical Intelligence Research for Advanced Innovation

Mission

Our mission is to save the lives of children across the globe through the development and application of artificial intelligence in clinical medicine, public health, and molecular biology.

Description

MIRAI (Medical Intelligence Research for Advanced Innovation -- mirai meaning "future" in Japanese) is a biomedical research department dedicated to advancing artificial intelligence in medicine and healthcare. Our work spans medical imaging to AI predictive models, with a focus on solutions applicable in low- and middle-income countries (LMICs) to improve patient outcomes and transform the delivery of care all over the world.

Research Areas

Medical Imaging and Diagnostics

We develop AI-driven systems for the analysis of medical imaging modalities -- including MRI, CT, and digital pathology -- to enhance disease detection, diagnostic accuracy, and clinical decision support.

Biomedical Data Science

We apply artificial intelligence and advanced statistical modeling to real-world clinical datasets to predict disease severity and assess emergency department utilization, with the goal of improving health outcomes in children.

Members

Kohji Okamura, Ph.D.
Tsunehiko Takamura, Ph.D.
Noriko Kawasaki
Miki Nobe
Michihiro Yamamura, M.D.
Natsuhiko Yamada, M.D.
Hiroaki Fukuse, M.D.
Hiroshi Yamanaka, M.D.
Iku Nakajima

Publications

Automated detection of mulberry bodies in urinary sediment for non-invasive Fabry disease screening: Yamanaka H, So T, Sakamoto N, Aoto S, Li XK, Wang Y, Shen Q, Migita O, Kosuga M, Okamura K
Clin. Chem. Lab. Med. [doi: 10.1515/cclm-2026-0345] (2026)
Detection of pediatric cataracts through non-invasive facial photography using deep convolutional neural networks for early diagnosis: Hayashi H, Kashizuka E, Sakata K, Motomiya N, Asakawa Y, Hayasaki M, Yokoi T, Yoshida T, Nishina S, Okamura K
BMC Ophthalmol. [doi: 10.1186/s12886-026-04795-9] (2026)
CYCS-related thrombocytopenia in three Japanese families with a novel variant in one family: Yamada N, Sakamoto A, Nagoshi R, Endo S, Yamamoto M, Saito S, Yamada Y, Uchiyama T, Yanagi K, Kaname T, Kunishima S, Ishiguro A
Int. J. Hematol. 123, 611-616 (2026)
Exploring deep learning and data requirements through image classification of Erigeron annuus and Erigeron philadelphicus: Yamanaka H, Okamura K
BMC Res. Notes 19, 127 (2026)
Chromosomal and hormonal factors involved in human sexual dimorphism: Fukami M, Okamura K, Sasaki S, Kagami M, Dateki S
Endocr. J. 73, 2, 175-181 (2026)
Privacy-preserving retrieval-augmented generation on local devices for regenerative medicine applications: Takamura T, Umezawa A
medRχiv 2025.10.20.25337146 (2025)
Removing extracellular matrix from the cell surface prior to seeding enhances the adhesion of primary human hepatocytes to the culture vessel: Miyai M, Tanaka-Yachi R, Aizawa K, Okamura K, Kusuhara H, Akutsu H, Nakamura K
Genes Cells 30, 6, e70059 (2025)
Applicability of the regression approach for histological multi-class grading in clear cell renal cell carcinoma: Shibata M, Umezawa A, Aoto S, Okamura K, Nasu M, Mizuno R, Oya M, Yura K, Mikami S
Regen. Ther. 28, 431-437 (2025)
Supervised machine learning of outbred mouse genotypes to predict hepatic immunological tolerance of individuals: Morita-Nakagawa M, Okamura K, Nakabayashi K, Inanaga Y, Shimizu S, Guo WZ, Fujino M, Li XK
Sci. Rep. 14, 1, 24399 (2024)
Proof of mechanism investigation of Transcutaneous auricular vagus nerve stimulation through simultaneous measurement of autonomic functions: a randomized controlled trial protocol: Katsunuma R, Takamura T, Yamada M, Sekiguchi A
Biopsychosoc. Med. 18, 15 (2024)
Machine learning trial to detect sex differences in simple sticker arts of 1606 preschool children: Matsubara K, Ohgami Y, Okamura K, Aoto S, Fukami M, Shimada Y
Minerva Pediatr. 76, 3, 343-349 (2024)
Systematic reduction of gray matter volume in anorexia nervosa, but relative enlargement with clinical symptoms in the prefrontal and posterior insular cortices: a multicenter neuroimaging study: Tose K, Takamura T, Isobe M, Hirano Y, Sato Y, Kodama N, Yoshihara K, Maikusa N, Moriguchi Y, Noda T, Mishima R, Kawabata M, Noma S, Takakura S, Gondo M, Kakeda S, Takahashi M, Ide S, Adachi H, Hamatani S, Kamashita R, Sudo Y, Matsumoto K, Nakazato M, Numata N, Hamamoto Y, Shoji T, Muratsubaki T, Sugiura M, Murai T, Fukudo S, Sekiguchi A
Mol. Psychiatry 29, 891-901 (2024)
Differentiation of large extracellular vesicles in oral fluid: combined protocol of small force centrifugation and sedimentation pattern analysis: Kawano T, Okamura K, Shinchi H, Ueda K, Nomura T, Shiba K
J. Extracell. Biol. 3, 2, e1143 (2024)
Integrator complex subunit 15 controls mRNA splicing and is critical for eye development: Azuma N, Yokoi T, Tanaka T, Matsuzaka E, Saida Y, Nishina S, Terao M, Takada S, Fukami M, Okamura K, Maehara K, Yamasaki T, Hirayama J, Nishina H, Handa H, Yamaguchi Y
Hum. Mol. Genet. 32, 12, 2032-2045 (2023)
Automated urinary sediment detection for Fabry disease using deep-learning algorithms: Uryu H, Migita O, Ozawa M, Kamijo C, Aoto S, Okamura K, Hasegawa F, Okuyama T, Kosuga M, Hata K
Mol. Genet. Metab. Rep. 33, 100921 (2022)
Collection of 2429 constrained headshots of 277 volunteers for deep learning: Aoto S, Hangai M, Ueno-Yokohata H, Ueda A, Igarashi M, Ito Y, Tsukamoto M, Jinno T, Sakamoto M, Okazaki Y, Hasegawa F, Ogata-Kawata H, Namura S, Kojima K, Kikuya M, Matsubara K, Taniguchi K, Okamura K
Sci. Rep. 12, 1, 3730 (2022)