Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks

Vladimir Iglovikov; Alexander Rakhlin; Alexandr A. Kalinin; Alexey Shvets

doi:10.1101/234120

CARVIEW

MOTORHOMES

Select Language

HTTP/2 301 date: Sun, 18 Jan 2026 05:57:50 GMT content-length: 0 location: https://doi.org/10.1101/234120 server: cloudflare vary: Origin expires: Mon, 19 Jan 2026 05:57:50 GMT permissions-policy: interest-cohort=(),browsing-topics=() cf-cache-status: DYNAMIC nel: {"report_to":"cf-nel","success_fraction":0.0,"max_age":604800} strict-transport-security: max-age=31536000; includeSubDomains; preload report-to: {"group":"cf-nel","max_age":604800,"endpoints":[{"url":"https://a.nel.cloudflare.com/report/v4?s=cP6vIuYRJBhPvX91TfYQrncabtoeVBrCArTX1Z7U5wsZO118QQZEQjbzPUQHsicT8iprGUT0rS1%2BLa76CJt4itlR0odbXA%3D%3D"}]} cf-ray: 9bfbdb6baaffc1c4-BLR alt-svc: h3=":443"; ma=86400 HTTP/2 302 date: Sun, 18 Jan 2026 05:57:50 GMT content-type: text/html;charset=utf-8 location: https://biorxiv.org/lookup/doi/10.1101/234120 server: cloudflare vary: Origin vary: Accept expires: Sun, 18 Jan 2026 06:11:00 GMT permissions-policy: interest-cohort=(),browsing-topics=() cf-cache-status: DYNAMIC nel: {"report_to":"cf-nel","success_fraction":0.0,"max_age":604800} strict-transport-security: max-age=31536000; includeSubDomains; preload report-to: {"group":"cf-nel","max_age":604800,"endpoints":[{"url":"https://a.nel.cloudflare.com/report/v4?s=UYyUWqcTbHSf6NzIXEdFtM7OIrabAdwH3SpUXvyNiLoHDjQ57csUulFQPImIXZoEayJe6C%2FA100xWsmWAKnnCqrlpIA1QQ%3D%3D"}]} cf-ray: 9bfbdb6beb18c1c4-BLR alt-svc: h3=":443"; ma=86400 HTTP/1.1 302 Found Date: Sun, 18 Jan 2026 05:57:50 GMT Content-Type: text/html; charset=iso-8859-1 Transfer-Encoding: chunked Connection: keep-alive server: cloudflare location: https://www.biorxiv.org/lookup/doi/10.1101/234120 cf-cache-status: DYNAMIC Nel: {"report_to":"cf-nel","success_fraction":0.0,"max_age":604800} Report-To: {"group":"cf-nel","max_age":604800,"endpoints":[{"url":"https://a.nel.cloudflare.com/report/v4?s=PnZ7hksyTGwQo%2FhZxmHtLfnwIvAvlbnzLFbZMgGc1XJZ2wu3UJmpY1CUfh33k5dq%2Fa12WtnWmGO%2Bf5bsgFx6C9wWQhnoAJxlr20s"}]} CF-RAY: 9bfbdb6c7df34055-BOM alt-svc: h3=":443"; ma=86400 HTTP/2 301 date: Sun, 18 Jan 2026 05:57:51 GMT content-type: text/html; charset=UTF-8 location: https://www.biorxiv.org/content/10.1101/234120v4 cf-ray: 9bfbdb6fad12b155-BLR x-content-type-options: nosniff x-content-type-options: nosniff x-drupal-cache: MISS expires: Sun, 18 Jan 2026 06:27:51 GMT cache-control: public, max-age=1800 x-varnish-ttl: pragma: no-cache vary: Accept-Encoding x-highwire-sitecode: biorxiv x-highwire-smart-code: biorxiv_production x-varnish: 693585449 x-varnish-cache: via: 1.1 varnish cf-cache-status: MISS set-cookie: __cf_bm=3TPJWwKDSHmr1CCePy5vJuNHSDlcVR3jjbupohvCbQU-1768715871-1.0.1.1-y8ueRBiqzrOo4NU0TWKgq5.sgQRfZqoaswky6akVlblarkFf67sMF_.slqAmPSu8cS1ksfs7xTEDw5xcSBVlJ.ebcspRnnsxNUENGESIRPk; path=/; expires=Sun, 18-Jan-26 06:27:51 GMT; domain=.www.biorxiv.org; HttpOnly; Secure; SameSite=None server: cloudflare HTTP/2 200 date: Sun, 18 Jan 2026 05:57:52 GMT content-type: text/html; charset=utf-8 content-encoding: gzip x-content-type-options: nosniff x-content-type-options: nosniff x-drupal-cache: MISS expires: Sun, 19 Nov 1978 05:00:00 GMT cache-control: no-cache, must-revalidate set-cookie: SSESS1dd6867f1a1b90340f573dcdef3076bc=XeSJbHqE_fmwZN6kaHr0KjnPJLeyJmagFmg8kmXtPH8; expires=Tue, 10-Feb-2026 09:31:11 GMT; path=/; domain=.biorxiv.org; secure; HttpOnly content-language: en x-frame-options: SAMEORIGIN x-generator: Drupal 7 (https://drupal.org) link: ; rel="canonical",; rel="shortlink" vary: Accept-Encoding x-highwire-sitecode: biorxiv x-highwire-smart-code: biorxiv_production x-varnish: 1891456695 age: 0 via: 1.1 varnish x-varnish-ttl: x-varnish-cache: cf-cache-status: DYNAMIC server: cloudflare cf-ray: 9bfbdb73092db155-BLR Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks | bioRxiv

Abstract

Skeletal bone age assessment is a common clinical practice to diagnose endocrine and metabolic disorders in child development. In this paper, we describe a fully automated deep learning approach to the problem of bone age assessment using data from the 2017 Pediatric Bone Age Challenge organized by the Radiological Society of North America. The dataset for this competition consists of 12,600 radiological images. Each radiograph in this dataset is an image of a left hand labeled with bone age and sex of a patient. Our approach utilizes several deep neural network architectures trained end-to-end. We use images of whole hands as well as specific parts of a hand for both training and prediction. This approach allows us to measure the importance of specific hand bones for automated bone age analysis. We further evaluate the performance of the suggested method in the context of skeletal development stages. Our approach outperforms other common methods for bone age assessment.

The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.