Whether the utilization of artificial intelligence (AI) during the interpretation of chest radiographs (CXRs) would affect the radiologists’ workload is of particular interest. Therefore, this prospective observational study aimed to observe how AI affected the reading times of radiologists in the daily interpretation of CXRs. Radiologists who agreed to have the reading times of their CXR interpretations collected from September to December 2021 were recruited. Reading time was defined as the duration in seconds from opening CXRs to transcribing the image by the same radiologist. As commercial AI software was integrated for all CXRs, the radiologists could refer to AI results for 2 months (AI-aided period). During the other 2 months, the radiologists were automatically blinded to the AI results (AI-unaided period). A total of 11 radiologists participated, and 18,680 CXRs were included. Total reading times were significantly shortened with AI use, compared to no use (13.3 s vs. 14.8 s, p < 0.001). When there was no abnormality detected by AI, reading times were shorter with AI use (mean 10.8 s vs. 13.1 s, p < 0.001). However, if any abnormality was detected by AI, reading times did not differ according to AI use (mean 18.6 s vs. 18.4 s, p = 0.452). Reading times increased as abnormality scores increased, and a more significant increase was observed with AI use (coefficient 0.09 vs. 0.06, p < 0.001). Therefore, the reading times of CXRs among radiologists were influenced by the availability of AI. Overall reading times shortened when radiologists referred to AI; however, abnormalities detected by AI could lengthen reading times.

Apparent diffusion coefficients (ADC) can help differentiate between central nervous system (CNS) lymphoma and Glioblastoma (GBM). However, overlap between ADCs for GBM and lymphoma have been reported because of various region of interest (ROI) methods. Our aim is to explore ROI method to provide the most reproducible results for differentiation. We studied 25 CNS lymphomas and 62 GBMs with three ROI methods: (1) ROI₁, whole tumor volume; (2) ROI₂, multiple ROIs; and (3) ROI₃, a single ROI. Interobserver variability of two readers for each method was analyzed by intraclass correlation(ICC). ADCs were compared between GBM and lymphoma, using two-sample t-test. The discriminative ability was determined by ROC analysis. ADCs from ROI₁ showed most reproducible results (ICC >0.9). For ROI₁, ADC_mean for lymphoma showed significantly lower values than GBM (p = 0.03). The optimal cut-off value was 0.98×10⁻³ mm²/s with 85% sensitivity and 90% specificity. For ROI₂, ADC_min for lymphoma was significantly lower than GBM (p = 0.02). The cut-off value was 0.69×10⁻³ mm²/s with 87% sensitivity and 88% specificity. ADC values were significantly dependent on ROI method. ADCs from the whole tumor volume had the most reproducible results. ADC_mean from the whole tumor volume may aid in differentiating between lymphoma and GBM. However, multi-modal imaging approaches are recommended than ADC alone for differentiation.

Controlled attenuation parameter (CAP) is a recently introduced, non-invasive and quantitative method to evaluate hepatic steatosis demonstrated in adults, but limited in obesity and not well evaluated in children. The aim of this study was to investigate the diagnostic performance for assessing hepatic steatosis grades using CAP in children based on MR proton density fat fraction (PDFF). Children evaluated for non-alcoholic fatty liver disease (NAFLD) who were assessed for PDFF and CAP were enrolled retrospectively. Hepatic steatosis grades 0–3 were classified according to PDFF using cutoff values of 6, 17.5, and 23.3%. Subgroup analyses were performed in non-obese and obese groups using the 95th percentile body mass index (BMI) as a cutoff and BMI30 group when BMI > 30 kg/m2. Pearson’s correlations between variables were also analyzed. In a total of 86 children, there were 53 in the obese group including 17 of the BMI30 group. CAP demonstrated 98.7% sensitivity and 80% specificity for diagnosing grades 1–3 vs. grade 0 using a cutoff value of 241 dB/m (area under the curve = 0.941, p < 0.001). The diagnostic performance for higher steatosis grades was suboptimal. CAP correlated with abdominal wall thickness in both obese (r = 0.549, p = 0.001) and non-obese (r = 0.386, p = 0.004) groups and did not correlate with PDFF in BMI30 group. In children with NAFLD, CAP showed excellent diagnostic performance for differentiating presence and absence of hepatic steatosis using a cutoff value of 241 dB/m. However, CAP was limited in evaluating grades of steatosis, especially in children with BMI > 30 kg/m2.