Enhanced enchondroma detection from x-ray images using deep learning: A step towards accurate and cost-effective diagnosis


Aydin Simsek S., Aydin A., Say F., Cengiz T., Ozcan C., Ozturk M., ...More

JOURNAL OF ORTHOPAEDIC RESEARCH, no.12, pp.2826-2834, 2024 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Publication Date: 2024
  • Doi Number: 10.1002/jor.25938
  • Journal Name: JOURNAL OF ORTHOPAEDIC RESEARCH
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, BIOSIS, CAB Abstracts, Veterinary Science Database
  • Page Numbers: pp.2826-2834
  • Ondokuz Mayıs University Affiliated: Yes

Abstract

This study investigates the automated detection of enchondromas, benign cartilage tumors, from x-ray images using deep learning techniques. Enchondromas pose diagnostic challenges due to their potential for malignant transformation and overlapping radiographic features with other conditions. Leveraging a data set comprising 1645 x-ray images from 1173 patients, a deep-learning model implemented with Detectron2 achieved an accuracy of 0.9899 in detecting enchondromas. The study employed rigorous validation processes and compared its findings with the existing literature, highlighting the superior performance of the deep learning approach. Results indicate the potential of machine learning in improving diagnostic accuracy and reducing healthcare costs associated with advanced imaging modalities. The study underscores the significance of early and accurate detection of enchondromas for effective patient management and suggests avenues for further research in musculoskeletal tumor detection.