Artificial Intelligence Algorithm Improves Radiologist Performance in Skeletal Age Assessment: A Prospective Multicenter Randomized Controlled Trial.

Background Previous studies suggest that use of artificial intelligence (AI) algorithms as diagnostic aids may improve the quality of skeletal age assessment, though these studies lack evidence from clinical practice. Purpose To compare the accuracy and interpretation time of skeletal age assessment on hand radiograph examinations with and without the use of an AI algorithm as a diagnostic aid. Materials and Methods In this prospective randomized controlled trial, the accuracy of skeletal age assessment on hand radiograph examinations was performed with (n = 792) and without (n = 739) the AI algorithm as a diagnostic aid. For examinations with the AI algorithm, the radiologist was shown the AI interpretation as part of their routine clinical work and was permitted to accept or modify it. Hand radiographs were interpreted by 93 radiologists from six centers. The primary efficacy outcome was the mean absolute difference between the skeletal age dictated into the radiologists' signed report and the average interpretation of a panel of four radiologists not using a diagnostic aid. The secondary outcome was the interpretation time. A linear mixed-effects regression model with random center- and radiologist-level effects was used to compare the two experimental groups. Results Overall mean absolute difference was lower when radiologists used the AI algorithm compared with when they did not (5.36 months vs 5.95 months; P = .04). The proportions at which the absolute difference exceeded 12 months (9.3% vs 13.0%, P = .02) and 24 months (0.5% vs 1.8%, P = .02) were lower with the AI algorithm than without it. Median radiologist interpretation time was lower with the AI algorithm than without it (102 seconds vs 142 seconds, P = .001). Conclusion Use of an artificial intelligence algorithm improved skeletal age assessment accuracy and reduced interpretation times for radiologists, although differences were observed between centers. Clinical trial registration no. NCT03530098 © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Rubin in this issue.

Improving Radiology Report Quality by Rapidly Notifying Radiologist of Report Errors.

Radiology report errors occur for many reasons including the use of pre-filled report templates, wrong-word substitution, nonsensical phrases, and missing words. Reports may also contain clinical errors that are not specific to the speech recognition including wrong laterality and gender-specific discrepancies. Our goal was to create a custom algorithm to detect potential gender and laterality mismatch errors and to notify the interpreting radiologists for rapid correction. A JavaScript algorithm was devised to flag gender and laterality mismatch errors by searching the text of the report for keywords and comparing them to parameters within the study's HL7 metadata (i.e., procedure type, patient sex). The error detection algorithm was retrospectively applied to 82,353 reports 4 months prior to its development and then prospectively to 309,304 reports 15 months after implementation. Flagged reports were reviewed individually by two radiologists for a true gender or laterality error and to determine if the errors were ultimately corrected. There was significant improvement in the number of flagged reports (pre, 198/82,353 [0.24%]; post, 628/309,304 [0.20%]; P = 0.04) and reports containing confirmed gender or laterality errors (pre, 116/82,353 [0.014%]; post, 285/309,304 [0.09%]; P < 0.0001) after implementing our error notification system. The number of flagged reports containing an error that were ultimately corrected improved dramatically after implementing the notification system (pre, 17/116 [15%]; post, 239/285 [84%]; P < 0.0001). We developed a successful automated tool for detecting and notifying radiologists of potential gender and laterality errors, allowing for rapid report correction and reducing the overall rate of report errors.

Comparing segmented ASL perfusion of vascular territories using manual versus semiautomated techniques in children with sickle cell anemia.

PURPOSE: Elevated cerebral blood flow (CBF) in sickle cell anemia (SCA) is an adaptive pathophysiologic response associated with decreased vascular reserve and increased risk for ischemia. We compared manual (M) and semiautomated (SA) vascular territory delineation to facilitate standardized evaluation of CBF in children with SCA. MATERIALS AND METHODS: ASL perfusion values from 21 children were compared for gray matter and white matter (WM) in vascular territories defined by M and SA delineation. SA delineated CBF was compared with clinical and hematologic variables acquired within 4 weeks of the MRI. RESULTS: CBF measurements from M (MCA 82 left, 79 right) and SA (MCA 81 left, 81 right) delineated territories were highly correlated (R = 0.99, P < 0.0001). Bland-Altman plots had close-fitting limits of agreement of -1.8 to -3.5 lower limit and 0 to 1.8 upper limit. SA vascular territory delineation was comparable to the expert delineation with a kappa index of 0.62-0.85 and was considerably faster. Median territorial CBF values did not differ by gender or age. WM perfusion in the posterior cerebral artery territories was positively correlated with degree of hemolysis (R = 0.58, P = 0.01 left, 0.73, P < 0.001 right) and negatively correlated with hemoglobin (R = -0.48; P = 0.03 left; -0.47; P = 0.04 right) and hemoglobin F (R = -0.42; P = .09 left; -0.47; P = 0.049 right). CONCLUSION: We established the validity of the SA method, which in our experience was much faster than the M method for delineation of vascular territories. Associations between CBF and hematologic variables may demonstrate pathophysiologic changes that contribute to clinical variation in CBF.