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Related Concept Videos

Classification of Bones01:18

Classification of Bones

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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...
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Bone Fracture Detection Using Deep Supervised Learning from Radiological Images: A Paradigm Shift.

Tanushree Meena1, Sudipta Roy1

  • 1Artificial Intelligence & Data Science, Jio Institute, Navi Mumbai 410206, India.

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|October 27, 2022
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Summary
This summary is machine-generated.

Deep learning (DL) aids radiologists in detecting bone fractures from radiographs, improving diagnosis accuracy. This review explores DL

Keywords:
artificial intelligencebone imagingcomputer visiondeep learningfracturesradiology

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Area of Science:

  • Orthopedics and Radiology
  • Medical Imaging Analysis
  • Artificial Intelligence in Healthcare

Background:

  • Musculoskeletal conditions affect 1.71 billion people globally, with fractures being a common and increasing concern.
  • Delayed or missed fracture diagnosis leads to complications and treatment delays.
  • Artificial intelligence (AI), particularly deep learning (DL), shows promise in medical image analysis for bone fracture detection.

Purpose of the Study:

  • To provide a systematic review of deep learning applications in bone imaging for fracture detection.
  • To highlight the potential of DL in assisting radiologists with diagnosing bone abnormalities.
  • To discuss challenges and future directions of DL in bone imaging.

Main Methods:

  • Systematic review of existing literature on deep learning in bone imaging.
  • Analysis of studies focusing on DL for fracture and bone disease diagnosis from radiographs.
  • Examination of DL methodologies applied in traumatology and orthopedics.

Main Results:

  • Deep learning demonstrates significant potential in enhancing the accuracy and efficiency of fracture detection in bone imaging.
  • DL algorithms can assist radiologists in identifying various bone abnormalities from radiographs.
  • Studies indicate DL's growing role in traumatology and orthopedics for diagnostic support.

Conclusions:

  • Deep learning offers a valuable tool to support radiologists in the timely and accurate diagnosis of bone fractures.
  • Addressing current challenges in DL implementation is crucial for its future integration into clinical practice.
  • The future of DL in bone imaging holds promise for improved patient care and outcomes in musculoskeletal conditions.