• Phichai Udombuathong Department of Orthopedic Surgery, Ananthamahidol Hospital, Lopburi, Thailand
  • Ruthasiri Srisawasdi Department of Orthopedic Surgery, Ananthamahidol Hospital, Lopburi, Thailand
  • Waravut Kesornsukhon Department of Computer and Information Science, Faculty of Applied Science, King Mongkut’s University of Technology North Bangkok, Bangkok, Thailand
  • San Ratanasanya Department of Computer and Information Science, Faculty of Applied Science, King Mongkut’s University of Technology North Bangkok, Bangkok, Thailand
Keywords: Artificial intelligence, Hip fracture


Background: Most hip fractures occur among elderly people. They are usually treated in the emergency room where orthopedic surgeons may not be readily available. The problem of delayed diagnosis and treatment results increase risks of further complications and mortality rate. Thus, applying artificial intelligence (AI) can assist physicians having limited experience to rapidly and confidently diagnose hip fractures using radiographs.

Objective: This study aimed to validate AI programs to assist diagnosing of hip fractures on plain radiographs.

Methods: This study employed a retrospective diagnostic study design. From 1 January 2015 to 31 December 2019, compiled ortho pelvis, anterior-posterior (AP) films from the diagnosis of hip fractures at Ananthamahidol Hospital were performed. The performance of the AI program was compared with one orthopedic surgeon who reviewed the same images. The accuracy, sensitivity and specificity of the diagnosis of hip fractures between the orthopedic surgeon and AI program were analyzed.

Results: In total, 217 patients were enrolled in this study. Of these, 56 (28.5%) were male and 161 (74.2%) female. Areas of hip fractures were as follow: intertrochanteric (108, 49.8%), femoral neck (102, 47.0%), subtrochanteric (6, 2.7%) and femoral head (1, 0.5%). The orthopedic surgeon and AI program revealed an accuracy of 93.59% (95%CI 90.8-95.73) vs. 81.24% (95% CI 77.17-84.85), sensitivity of 90.30% (95% CI 85.60-93.90) vs. 89.40% (95%CI 84.50-93.20) and specificity of 97.10% (95%CI 93.60-98.90) vs. 72.5% (95%CI 65.90-78.50), respectively.

Conclusion: Our results showed that the AI model (VGG16) showed a sensitivity of 89.40% vs. 90.30% obtained from the orthopedic surgeon. Thus, improvement in the sensitivity and specificity of AI software is further required. In the future, AI models have the potential as useful tools for emergent screening and evaluation of patients with hip fractures using plain radiographs, especially in the Emergency Department where orthopedic surgeons may not be readily available.


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How to Cite
Udombuathong P, Srisawasdi R, Kesornsukhon W, Ratanasanya S. APPLICATION OF ARTIFICIAL INTELLIGENCE TO ASSIST HIP FRACTURE DIAGNOSIS USING PLAIN RADIOGRAPHS. J Southeast Asian Med Res [Internet]. 2022Apr.8 [cited 2022May26];60:e0111. Available from:
Original Articles