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OBJECTIVES: To develop and validate an artificial intelligence (AI) system for measuring and detecting signs of carpal instability on conventional radiographs. MATERIALS AND METHODS: Two 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. RESULTS: Dataset 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. CONCLUSION: This 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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INTRODUCTION: This study evaluated the value of pre-biopsy MRI and target biopsy in detection of significant prostate cancer in a peripheral center. METHODS: A retrospective study included all patients of whom a MRI of the prostate was performed before biopsy, initial and repeated biopsy, between June 2016 and May 2017. Patients underwent transrectal ultrasound guided 8-12 cores prostate biopsy and cognitive fusion target biopsy was performed if a suspicious lesion was seen on MRI. The prostate cancer detection was compared between the MRI cognitive target biopsy and standard random biopsy. RESULTS: In a total of 265 patients a MRI was performed of whom 115 underwent prostate biopsy, 96 patients underwent MRI before initial biopsies and 19 patients had previous negative biopsies. In the initial biopsy group 83 MRI's were abnormal and only 7 (8.4%) target biopsies had an additional value in detecting or upstaging prostate cancer. Prostate cancer was found in 4 of 13 (30.8%) normal MRI's. In the prior negative biopsy group, 4 of 18 abnormal MRI's had an additional value in upstaging or detecting prostate cancer. CONCLUSION: In this study the pre-biopsy MRI had a limited additional value compared to standard biopsy in detecting or upstaging prostate cancer.
