Evidence map›Paper›PMID 42570094›Full record

ArticlePediatric radiology2026

Bone age estimation from chest radiographs using deep neural networks: a proof-of-concept study.

Hidehito Ota, Takaya Hanawa, Hiroshi Yoshihara, Ayami Sato, Natsuho Adachi, Yumiko Chiba, Hiroyuki Tanaka

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Article in Pediatric radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Hidehito OtaDepartment of Pediatrics, University of Tokyo Hospital, 7-3-1, Hongo, Bunkyo, Tokyo, 113-8655, Japan. hioota-tky@umin.ac.jp.
Takaya HanawaDepartment of Pediatrics, University of Tokyo Hospital, 7-3-1, Hongo, Bunkyo, Tokyo, 113-8655, Japan.
Hiroshi YoshiharaDepartment of Health Policy and Public Health, Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo, Japan.
Ayami SatoDepartment of Pediatrics, University of Tokyo Hospital, 7-3-1, Hongo, Bunkyo, Tokyo, 113-8655, Japan.
Natsuho AdachiDepartment of Pediatrics, University of Tokyo Hospital, 7-3-1, Hongo, Bunkyo, Tokyo, 113-8655, Japan.
Yumiko ChibaDepartment of Pediatrics, University of Tokyo Hospital, 7-3-1, Hongo, Bunkyo, Tokyo, 113-8655, Japan.
Hiroyuki TanakaDepartment of Pediatrics, University of Tokyo Hospital, 7-3-1, Hongo, Bunkyo, Tokyo, 113-8655, Japan. tanakah-tky@umin.ac.jp.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBone age (BA) is the gold standard for skeletal maturity assessment but is not routinely incorporated into pediatric growth evaluation workflow because it requires dedicated hand radiographs and specialist interpretation.

objectiveTo develop an estimation model for BA from chest radiographs using deep neural networks. MATERIALS AND

methodsWe retrospectively analyzed children aged 3-15 years who underwent both chest and hand radiography at a tertiary center over 20 years. Patients with skeletal dysplasia or chest wall abnormalities were excluded. Reference BA was determined from hand radiographs by pediatric endocrinologists using the Tanner-Whitehouse 2 radius-ulna-short bones (TW2-RUS) method. Three pretrained deep neural networks were fine-tuned to estimate BA from chest radiographs using sex-non-considering models and sex-considering models. Model performance was evaluated using the intraclass correlation coefficient (ICC), root mean squared error (RMSE), and related metrics.

resultsOf 180 screened patients, 101 were included, yielding 237 chest radiographs. Estimated BA showed good concordance with the reference standard, with ICCs up to 0.81 (95% CI 0.48-0.97) for sex-non-considering models and 0.87 (95% CI 0.63-0.99) for sex-considering models. Corresponding RMSEs were 1.52 (95% CI 0.67-2.20) and 1.30 (95% CI 0.56-1.95), respectively.

conclusionThis proof-of-concept study demonstrates the feasibility of estimating BA from pediatric chest radiographs using deep neural networks. These findings suggest potential opportunistic use when dedicated hand radiographs are unavailable.

Indexed as

Artificial intelligenceBone ageDeep learningOpportunistic screeningSkeletal maturation

Identifiers

PMID42570094

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