Accessibility settings

This paper is in the following e-collection/theme issue:

Corrigenda and Addenda (2)

Published on in Vol 5 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/101732, first published .
JMIR Publications logo: Advancing Digital Health & Open Science

Correction: Deep Learning for Age Estimation and Sex Prediction Using Mandibular-Cropped Cephalometric Images: Comparative Model Development and Validation Study

Correction: Deep Learning for Age Estimation and Sex Prediction Using Mandibular-Cropped Cephalometric Images: Comparative Model Development and Validation Study

Authors of this article:

Vitria Wuri Handayani1, 2 Author Orcid Image ;   Mieke Sylvia Margaretha Amiatun Ruth3 Author Orcid Image ;   Riries Rulaningtyas4 Author Orcid Image ;   Arofi Kurniawan3, 5 Author Orcid Image ;   Bayu Azra Yudhantorro6 Author Orcid Image ;   Ahmad Yudianto4, 5 Author Orcid Image
Article Authors Cited by Tweetations Metrics

    1Faculty of Medicine, Universitas Airlangga, Surabaya, Indonesia

    2Nursing Department, Poltekkes Kemenkes Pontianak, Pontianak, Indonesia

    3Division of Forensic Odontology, Faculty of Dental Medicine, Universitas Airlangga, Surabaya, Indonesia

    4Forensics and Medicolegal Department, Faculty of Medicine, Universitas Airlangga, Surabaya, East Java 60132, Indonesia, Surabaya, Indonesia

    5Postgraduate School, Universitas Airlangga, Surabaya, Indonesia

    6Department of Information Systems, Institut Sepuluh Nopember, Surabaya, Indonesia

    *these authors contributed equally

    Corresponding Author:

    Ahmad Yudianto, MD, PhD



    In “Deep Learning for Age Estimation and Sex Prediction Using Mandibular-Cropped Cephalometric Images: Comparative Model Development and Validation Study” [1], the authors made one change.

    In the Discussion section, the following sentence has been revised:

    Similarly, Gao and Tang showed that deep learning frameworks integrating cephalometric landmarks achieve greater accuracy when attention mechanisms prioritize mandibular regions, as these structures carry distinctive morphological cues critical for demographic prediction.

    The sentence has been revised to the following:

    Similarly, Küchler et al. [2] demonstrated that deep learning frameworks integrating cephalometric landmarks achieve greater accuracy when attention mechanisms prioritize mandibular regions, as these structures carry distinctive morphological cues critical for demographic prediction.

    In response, the following reference has been added to the paper as reference 23 (included here as reference 2):

    Küchler EC, Krohn PP, Efeiche EGC, et al. Age estimation of children and adolescents from mandibles using machine learning. Sci Rep. Oct 7, 2025;15(1). [doi: 10.1038/s41598-025-21221-0] [Medline: 41057564]

    The correction will appear in the online version of the paper on the JMIR Publications website, together with the publication of this correction notice. Because this was made after submission to PubMed, PubMed Central, and other full-text repositories, the corrected article has also been resubmitted to those repositories.

    1. Handayani VW, Margaretha Amiatun Ruth MS, Rulaningtyas R, Kurniawan A, Yudhantorro BA, Yudianto A. Deep learning for age estimation and sex prediction using mandibular-cropped cephalometric images: comparative model development and validation study. JMIR AI. Mar 18, 2026;5:e84984. [CrossRef] [Medline]
    2. Küchler EC, Krohn PP, Efeiche EGC, et al. Age estimation of children and adolescents from mandibles using machine learning. Sci Rep. Oct 7, 2025;15(1):35021. [CrossRef] [Medline]

    This is a non–peer-reviewed article. submitted 19.May.2026; accepted 19.May.2026; published 15.Jun.2026.

    Copyright

    ©Vitria Wuri Handayani, Mieke Sylvia Margaretha Amiatun Ruth, Riries Rulaningtyas, Arofi Kurniawan, Bayu Azra Yudhantorro, Ahmad Yudianto. Originally published in JMIR AI (https://ai.jmir.org), 15.Jun.2026.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR AI, is properly cited. The complete bibliographic information, a link to the original publication on https://www.ai.jmir.org/, as well as this copyright and license information must be included.