Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Classification of Bones

Classification of Bones

The bones of the human skeletal system are of varied shapes, sizes, and functions. They can be classified based on their shape and function into four major classes: long bones, short bones, flat bones, and irregular bones. Some classifications include a fifth type, the sesamoid bones, as a separate class, whereas others categorize them under short bones.
Long and Short Bones
The appendicular skeleton, particularly the upper and lower limbs, is primarily made of long and short bones. The long...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Editorial. Training pediatric neurosurgeons: but where in the world do they go?

Journal of neurosurgery. Pediatrics·2026

Same author

Deep learning-derived pericardial adipose tissue by electrocardiogram-gated cardiac computed tomography predicts cardiovascular events beyond coronary calcium score.

American journal of preventive cardiology·2026

Same author

Development and nested cross-validation of machine learning models for predicting transfusion requirements in pediatric craniosynostosis surgery.

Journal of plastic, reconstructive & aesthetic surgery : JPRAS·2026

Same author

A call for action: The need to quantify the "-itis" in primary sclerosing cholangitis.

Journal of hepatology·2026

Same author

Clinical-grade cryopreservation unlocks transplant-ready human pancreatic and stem cell-derived islets for diabetes therapy.

bioRxiv : the preprint server for biology·2026

Same author

MRI Deep Learning for Differentiating Glioblastoma, IDH Wild-type from Central Nervous System Diffuse Large B-cell Lymphoma.

Cancer research communications·2026

Search research articles

Related Experiment Video

Updated: Jun 28, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Towards Automated Craniosynostosis Diagnosis Using EfficientNet-Based Artificial Intelligence Models: A Two-Class and

Varekan Keishing^1,2, Edward S Ahn³, Zenith Khashim⁴

¹Mayo Clinic Artificial Intelligence Lab, Department of Radiology, Mayo Clinic, 200, 1st Street, S.W., Rochester, MN, 55905, USA.

Journal of Imaging Informatics in Medicine

|January 13, 2026

Summary

Artificial intelligence (AI) models can now detect craniosynostosis, a birth defect, using standard 2D head photos. This non-invasive AI approach offers accurate early diagnosis, improving surgical outcomes and accessibility for infants.

Keywords:

Artificial intelligence Craniosynostosis EfficientNet Image classification Machine learning

More Related Videos

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Published on: February 23, 2024

Related Experiment Videos

Last Updated: Jun 28, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Published on: February 23, 2024

Area of Science:

Medical Imaging
Artificial Intelligence
Pediatric Surgery

Background:

Craniosynostosis, premature fusion of infant skull bones, can cause disfigurement and impede brain growth.
Current diagnosis often relies on computed tomography (CT), involving radiation exposure and potential delays.
Early detection is crucial for less invasive surgical interventions and improved patient outcomes.

Purpose of the Study:

To evaluate the efficacy of state-of-the-art AI architectures for detecting craniosynostosis using standard 2D clinical photographs.
To compare the performance of EfficientNet-B7, ResNet-50, and ResNet-152 models in binary and multi-class craniosynostosis classification.
To establish a non-invasive, radiation-free diagnostic tool for craniosynostosis.

Main Methods:

Trained and tested EfficientNet-B7, ResNet-50, and ResNet-152 models on 688 clinical 2D photographs.
Utilized five random seeds for robust evaluation of binary and multi-class classification tasks.
Focused on standard photographs, avoiding specialized imaging like X-ray, CT, or photogrammetry.

Main Results:

All models achieved high accuracy in binary classification, with EfficientNet-B7 reaching 99.7% accuracy and perfect sensitivity.
EfficientNet-B7 demonstrated superior performance in multi-class classification (96.6% accuracy), outperforming ResNet-50.
AI models successfully classified craniosynostosis subtypes using only standard 2D images.

Conclusions:

AI models trained on 2D photographs provide accurate, cost-effective, and non-invasive craniosynostosis detection and classification.
This approach supports timely intervention, potentially expanding access to care in diverse healthcare settings.
This study pioneers high-accuracy craniosynostosis classification using readily available clinical photographs.