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Related Concept Videos

Classification of Bones01:18

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...

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Related Experiment Video

Updated: Jun 18, 2026

In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy
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Assessing Acetabular Index Angle in Infants: A Deep Learning-Based Novel Approach.

Farmanullah Jan1, Atta Rahman1, Roaa Busaleh1

  • 1Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia.

Journal of Imaging
|November 24, 2023
PubMed
Summary
This summary is machine-generated.

A new deep learning framework accurately detects developmental dysplasia of the hip (DDH) in infants using X-rays. This computational tool aids specialists in objective diagnosis, improving early detection and treatment success rates for this hip disorder.

Keywords:
Detectron2Mask-RCNNRCNNacetabular indexdeep learningdevelopment dysplasia of the hip (DDH)key point

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Pediatric Orthopedics

Background:

  • Developmental dysplasia of the hip (DDH) is a common infant hip disorder requiring early diagnosis for effective treatment.
  • Accurate diagnosis of DDH relies on expert interpretation of pelvic X-ray scans, which can be challenging without specialized training.
  • Current diagnostic methods for DDH may lack objectivity and consistency.

Purpose of the Study:

  • To develop and validate a computational framework for the objective detection of DDH in infant pelvic X-rays.
  • To utilize a deep learning approach for precise measurement of the acetabular index angle, a key indicator of DDH.
  • To create an accessible tool for medical specialists to aid in the early and accurate diagnosis of DDH.

Main Methods:

  • A two-stage deep learning pipeline combining instance segmentation and keypoint detection models was employed.
  • The framework analyzes infant pelvic X-ray images to identify hip abnormalities indicative of DDH.
  • The system quantifies the acetabular index angle, providing objective diagnostic metrics.

Main Results:

  • The deep learning model demonstrated high accuracy in measuring the acetabular angle, with an average pixel error of 2.862 ± 2.392.
  • The acetabular angle measurement error was within a range of 2.402 ± 1.963° compared to ground truth annotations.
  • The developed model provides an objective and unified approach to DDH diagnosis.

Conclusions:

  • The proposed deep learning framework offers a reliable and objective method for detecting DDH in infants.
  • Integration into a mobile application will enhance accessibility for medical specialists, reducing diagnostic burden.
  • This technology has the potential to improve early DDH detection rates, leading to better patient outcomes.