Prenatal diagnosis of pontocerebellar hypoplasia with postnatal follow-up.

OBJECTIVE: To describe the MR features enabling prenatal diagnosis of pontocerebellar hypoplasia (PCH). METHOD: This was a retrospective single monocentre study. The inclusion criteria were decreased cerebellar biometry on dedicated neurosonography and available fetal Magnetic Resonance Imaging (MRI) with PCH diagnosis later confirmed either genetically or clinically on post-natal MRI or by autopsy. The exclusion criteria were non-available MRI and sonographic features suggestive of a known genetic or other pathologic diagnosis. The collected data were biometric or morphological imaging parameters, clinical outcome, termination of pregnancy (TOP), pathological findings and genetic analysis (karyotyping, chromosomal microarray, DNA sequencing targeted or exome). PCH was classified as classic, non-classic, chromosomal, or unknown type. RESULTS: Forty-two fetuses were diagnosed with PCH, of which 27 were referred for decreased transverse cerebellar diameter at screening ultrasound. Neurosonography and fetal MRI were performed at a mean gestational age of 29 + 4 and 31 + 0 weeks, respectively. Termination of pregnancy occurred. Pregnancy was terminated in 24 cases. Neuropathological examination confirmed the diagnosis in 24 cases and genetic testing identified abnormalities in 29 cases (28 families, 14 chromosomal anomaly). Classic PCH is associated with pontine atrophy and small MR measurements decreasing with advancing gestation. CONCLUSION: This is the first large series of prenatally diagnosed PCHs. Our study shows the essential contribution of fetal MRI to the prenatal diagnosis of PCH. Classic PCHs are particularly severe and are associated with certain MR features.

Assessment of an artificial intelligence aid for the detection of appendicular skeletal fractures in children and young adults by senior and junior radiologists.

BACKGROUND: As the number of conventional radiographic examinations in pediatric emergency departments increases, so, too, does the number of reading errors by radiologists. OBJECTIVE: The aim of this study is to investigate the ability of artificial intelligence (AI) to improve the detection of fractures by radiologists in children and young adults. MATERIALS AND METHODS: A cohort of 300 anonymized radiographs performed for the detection of appendicular fractures in patients ages 2 to 21 years was collected retrospectively. The ground truth for each examination was established after an independent review by two radiologists with expertise in musculoskeletal imaging. Discrepancies were resolved by consensus with a third radiologist. Half of the 300 examinations showed at least 1 fracture. Radiographs were read by three senior pediatric radiologists and five radiology residents in the usual manner and then read again immediately after with the help of AI. RESULTS: The mean sensitivity for all groups was 73.3% (110/150) without AI; it increased significantly by almost 10% (P<0.001) to 82.8% (125/150) with AI. For junior radiologists, it increased by 10.3% (P<0.001) and for senior radiologists by 8.2% (P=0.08). On average, there was no significant change in specificity (from 89.6% to 90.3% [+0.7%, P=0.28]); for junior radiologists, specificity increased from 86.2% to 87.6% (+1.4%, P=0.42) and for senior radiologists, it decreased from 95.1% to 94.9% (-0.2%, P=0.23). The stand-alone sensitivity and specificity of the AI were, respectively, 91% and 90%. CONCLUSION: With the help of AI, sensitivity increased by an average of 10% without significantly decreasing specificity in fracture detection in a predominantly pediatric population.