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.

Development and feasibility assessment of telephone-delivered supportive care to improve outcomes for patients with colorectal cancer: pilot study of the CONNECT intervention.

GOALS OF WORK: This study aims to describe a pilot study of the feasibility, acceptability and likely impact of a nurse-delivered, telephone intervention to reduce unmet need and improve quality of life for surgical patients with colorectal cancer. MATERIALS AND METHODS: The CONNECT intervention comprises five standardised calls over 6 months commencing on day 3 post-discharge. A prospective non-randomised control trial with patients who had surgery for colorectal cancer at Royal Prince Alfred Hospital, Sydney between July and December 2006 was conducted. Patients completed a telephone interview with an independent researcher at 1, 3 and 6 months to assess study outcomes, including unmet need (Supportive Care Needs Survey), psychological distress and quality of life (FACT-C). Patients' views of the intervention were ascertained. MAIN RESULTS: Forty-one patients participated, 20 in the intervention period. Intervention calls were successfully completed with 85% or more of patients at each of the five time points. Mean call duration ranged from 14-19 min with the highest number of needs (27 for 20 patients) identified on day 3. Patients indicated that the timing of the calls was appropriate and the majority (85%) felt the number of calls was sufficient. There were promising trends in outcomes. For both patient groups, there were clinically meaningful improvements in FACT-C scores over time, with a larger improvement in the intervention group (20.4 points) than the control group (11.7). CONCLUSIONS: The CONNECT intervention was found to be feasible and acceptable to patients. A larger randomised trial is underway to establish its effectiveness to improve patient outcomes.