Objectives: To investigate the impact of deep learning (DL) on radiologists’ detection accuracy and reading efficiency of rib fractures on CT. Methods: Blunt chest trauma patients (n = 198) undergoing thin-slice CT were enrolled. Images were read by two radiologists (R1, R2) in three sessions: S1, unassisted reading; S2, assisted by DL as the concurrent reader; S3, DL as the second reader. The fractures detected by the readers and total reading time were documented. The reference standard for rib fractures was established by an expert panel. The sensitivity and false-positives per scan were calculated and compared among S1, S2, and S3. Results: The reference standard identified 865 fractures on 713 ribs (102 patients) The sensitivity of S1, S2, and S3 was 82.8, 88.9, and 88.7% for R1, and 83.9, 88.7, and 88.8% for R2, respectively. The sensitivity of S2 and S3 was significantly higher compared to S1 for both readers (all p < 0.05). The sensitivity between S2 and S3 did not differ significantly (both p > 0.9). The false-positive per scan had no difference between sessions for R1 (p = 0.24) but was lower for S2 and S3 than S1 for R2 (both p < 0.05). Reading time decreased by 36% (R1) and 34% (R2) in S2 compared to S1. Conclusions: Using DL as a concurrent reader can improve the detection accuracy and reading efficiency for rib fracture. Advances in knowledge: DL can be integrated into the radiology workflow to improve the accuracy and reading efficiency of CT rib fracture detection.

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Abstract. The aim of the present study was to evaluate the utility of diffusion-weighted magnetic resonance imaging (DWI) in the diagnosis of common renal tumors. Conventional magnetic resonance imaging and DWI were performed on 85 patients with renal lesions (54 renal carcinoma and 31 renal angiomyolipoma cases). The apparent diffusion coefficient (ADC) values in each case at b=800 sec/mm 2 were measured in the ADC maps using a statistical software package. The 54 cases of renal cell carcinoma showed a high signal intensity in the parenchyma, and the 31 renal angiomyolipoma cases showed a well-defined mixed signal intensity on DWI. The soft-tissue component showed a high signal intensity and the fat tissue showed a low signal intensity on DWI. When the b-value was set to 800 sec/mm 2 , the mean ADC was significantly lower in the renal carcinoma cases than in the renal angiomyolipoma cases. In conclusion, the measurement of ADC on DWI can reveal the structure of renal tumors, which is beneficial in diagnosing and determining the prognosis of benign and malignant renal tumors.

Joubert syndrome (JS) and JS‐related disorders (JSRD) are a group of neurodevelopmental diseases that share the “molar tooth sign” on axial brain magnetic resonance imaging (MRI), accompanied by cerebellar vermis hypoplasia, ataxia, hypotonia, and developmental delay. To identify variants responsible for the clinical symptoms of a Chinese family with JS and to explore the genotype–phenotype associations, we conducted a series of clinical examinations, including blood tests, brain MRI scans, ultrasound imaging, and ophthalmologic examination. Genomic DNA was extracted from the peripheral blood of the six‐person family, and the pathogenic variants were detected by whole‐exome sequencing (WES) and verified by Sanger sequencing. WES revealed two novel compound heterozygous variants in CPLANE1: c.1270C>T (p.Arg424*) in exon 10 and c.8901C>A (p.Tyr2967*) in exon 48 of one child, inherited from each parent. Both variants were absent in ethnically matched Chinese control individuals and were either absent or present at very low frequencies in public databases, suggesting that these variants could be the pathogenic triggers of the JS phenotype. Notably, these CPLANE1 sequence variants were related to the pathogenesis of autosomal recessive JS in this study. The newly discovered variants expand the mutation spectrum of CPLANE1, which assists in understanding the molecular mechanism underlying JS and improving the recognition of genetic counseling, particularly for families with a history of autosomal recessive JS.