Performances of a deep learning algorithm for the detection of fracture, dislocation, elbow joint effusion, focal bone lesions on trauma X-rays

Regnard et. al

EuSoMII 2021

Published In

EuSoMII 2021 (2021)

Authors

Nor-Eddine Regnard, Jeanne Ventre, Boubekeur Lanseur, Louis Lassalle, Aurélien Lambert, Benajmin Dallaudière, Antoine Feydy

Abstract

Short summary :

An artificial intelligence (AI) software that detects skeletal lesions on standard X-rays can help radiologists avoid diagnostic errors.

Purpose/Objectives:

To appraise the performances of an AI trained to detect and localize skeletal lesions and compare them to the routine radiological interpretation.

Methods and materials:

We retrospectively collected all radiographic examinations with the associated radiologists’ reports performed after a traumatic injury during 3 consecutive months (January to March 2017) in a private imaging group of 14 centers. Each examination was analyzed by an AI (BoneView, Gleamer) and its results were compared to those of the radiologists’ reports. In case of discrepancy, the examination was reviewed by a senior skeletal radiologist to settle on the presence of fractures, dislocations, elbow effusions, and focal bone lesions (FBL).

Lesion-wise sensitivity, specificity, and NPV of the AI and of the radiologists’ report were calculated for each lesion type.

The study received IRB approval n°CRM-2106-177.

Results:

A total of 4774 exams were included in the study.  Lesion-wise sensitivity was 73.7% for the radiologists’ reports vs. 98.1% for the AI (+24.4 points) for fracture detection, 63.3% vs. 89.9% (+26.6 points) for dislocation detection, 84.7% vs. 91.5% (+6.8 points) for elbow effusion detection, and 16.1% vs. 98.1% (+82 points) for FBL detection. The specificity of the radiologists’ reports was always 100% whereas AI specificity was 88%, 99.1%, 99.8%, 95.6% for fractures, dislocations, elbow effusions, and FBL respectively. The NPV was measured at 99.5% for fractures, 99.8% for dislocations, and 99.9% for elbow effusions and FBL.

Conclusion:

AI has the potential to prevent diagnosis errors by detecting lesions that were initially missed in the radiologists’ report. The main limitations are that the performance of the AI was calculated stand-alone and that the concordant examinations between the AI and the radiologists’ reports were not reviewed by the Ground Truth.

Keywords:

Deep learning ; fracture ; elbow effusion ; dislocation ; focal bone lesion