ArticleEuropean radiology2024
Artificial intelligence for automated detection and measurements of carpal instability signs on conventional radiographs.
Article in European radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed, 6 citations in OpenAlex.
- The Emergence of Applied Artificial Intelligence in the Realm of Value Based Musculoskeletal Care.Current reviews in musculoskeletal medicine · 2025Review
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Authors and funding
13 authors at 3 institutions in 2 countries.
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No grant is acknowledged in the PubMed record.
Abstract
objectivesTo develop and validate an artificial intelligence (AI) system for measuring and detecting signs of carpal instability on conventional radiographs. MATERIALS AND
methodsTwo case-control datasets of hand and wrist radiographs were retrospectively acquired at three hospitals (hospitals A, B, and C). Dataset 1 (2178 radiographs from 1993 patients, hospitals A and B, 2018-2019) was used for developing an AI system for measuring scapholunate (SL) joint distances, SL and capitolunate (CL) angles, and carpal arc interruptions. Dataset 2 (481 radiographs from 217 patients, hospital C, 2017-2021) was used for testing, and with a subsample (174 radiographs from 87 patients), an observer study was conducted to compare its performance to five clinicians. Evaluation metrics included mean absolute error (MAE), sensitivity, and specificity.
resultsDataset 2 included 258 SL distances, 189 SL angles, 191 CL angles, and 217 carpal arc labels obtained from 217 patients (mean age, 51 years ± 23 [standard deviation]; 133 women). The MAE in measuring SL distances, SL angles, and CL angles was respectively 0.65 mm (95%CI: 0.59, 0.72), 7.9 degrees (95%CI: 7.0, 8.9), and 5.9 degrees (95%CI: 5.2, 6.6). The sensitivity and specificity for detecting arc interruptions were 83% (95%CI: 74, 91) and 64% (95%CI: 56, 71). The measurements were largely comparable to those of the clinicians, while arc interruption detections were more accurate than those of most clinicians.
conclusionThis study demonstrates that a newly developed automated AI system accurately measures and detects signs of carpal instability on conventional radiographs. CLINICAL RELEVANCE STATEMENT: This system has the potential to improve detections of carpal arc interruptions and could be a promising tool for supporting clinicians in detecting carpal instability.
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