Background Early pregnancy ultrasound scans are usually performed by nonexpert examiners in obstetrics/gynecology (OB/GYN) emergency departments. Establishing the precise diagnosis of pregnancy location is key for appropriate management of early pregnancies, and experts are usually able to locate a pregnancy in the first scan. A decision-support system based on a semantic, expert-validated knowledge base may improve the diagnostic performance of nonexpert examiners for early pregnancy transvaginal ultrasound. Objective This study aims to evaluate a novel Intelligent Scan Assistant System for early pregnancy ultrasound to diagnose the pregnancy location and determine the image quality. Methods Two trainees performed virtual transvaginal ultrasound examinations of early pregnancy cases with and without the system. The ultrasound images and reports were blindly reviewed by two experts using scoring methods. A diagnosis of pregnancy location and ultrasound image quality were compared between scans performed with and without the system. Results Each trainee performed a virtual vaginal examination for all 32 cases with and without use of the system. The analysis of the 128 resulting scans showed higher quality of the images (quality score: +23%; P <.001), less images per scan (4.6 vs 6.3 [without the CDSS]; P <.001), and higher confidence in reporting conclusions (trust score: +20%; P <.001) with use of the system. Further, use of the system cost an additional 8 minutes per scan. We observed a correct diagnosis of pregnancy location in 39 (61%) and 52 (81%) of 64 scans in the nonassisted mode and assisted mode, respectively. Additionally, an exact diagnosis (with precise ectopic location) was made in 30 (47%) and 49 (73%) of the 64 scans without and with use of the system, respectively. These differences in diagnostic performance (+20% for correct location diagnosis and +30% for exact diagnosis) were both statistically significant ( P =.002 and P <.001, respectively). Conclusions The Intelligent Scan Assistant System is based on an expert-validated knowledge base and demonstrates significant improvement in early pregnancy scanning, both in diagnostic performance (pregnancy location and precise diagnosis) and scan quality (selection of images, confidence, and image quality).

Background and Objectives: Anti-c is the third red blood cell antibody responsible for haemolytic disease of the foetus and newborn (HDFN) requiring intrauterine transfusion. We aimed to identify risk factors associated with HDFN and severe HDFN due to Rhc maternal-foetal incompatibility. Materials and Methods:A retrospective cohort study was conducted in Paris and the surrounding area (France), between 2013 and 2015. We included mothers and their children managed by the National Reference Centre in Perinatal Hemobiology for alloimmunization and maternal-foetal incompatibility for the Rhc antigen (N = 121). We conducted bivariate analyses to assess a relationship between perinatal factors (e.g., titre and concentration of anti-c antibodies, direct antiglobulin test) and HDFN, its severity and duration.Results: The incidence of HDFN was 30% (n = 36), including 11% of severe HDFN (n = 13). Seven percent (n = 9) of neonates received at least one transfusion during the first week and 21% (n = 26) after this period until 3 weeks of life. During pregnancy, a concentration ≥7.5 IU/ml and a titre ≥4 and above were associated with HDFN and severe HDFN (p < 0.05). At birth, the high intensity of the quantitative direct antiglobulin test was associated with HDFN and severe HDFN (p < 0.05). A concentration ≥15 IU/ml is the best factor (area under curve [AUC] = 0.78) in predicting HDFN, followed by a titre ≥8 (AUC = 0.76). Conclusion:Anti-c alloimmunization causes neonatal anaemia, which is often belated.Paediatricians have to be aware of these risk factors and organize prolonged monitoring of neonates.

BACKGROUND Early pregnancy ultrasound scans are usually performed by nonexpert examiners in obstetrics/gynecology (OB/GYN) emergency departments. Establishing the precise diagnosis of pregnancy location is key for appropriate management of early pregnancies, and experts are usually able to locate a pregnancy in the first scan. A decision-support system based on a semantic, expert-validated knowledge base may improve the diagnostic performance of nonexpert examiners for early pregnancy transvaginal ultrasound. OBJECTIVE This study aims to evaluate a novel Intelligent Scan Assistant System for early pregnancy ultrasound to diagnose the pregnancy location and determine the image quality. METHODS Two trainees performed virtual transvaginal ultrasound examinations of early pregnancy cases with and without the system. The ultrasound images and reports were blindly reviewed by two experts using scoring methods. A diagnosis of pregnancy location and ultrasound image quality were compared between scans performed with and without the system. RESULTS Each trainee performed a virtual vaginal examination for all 32 cases with and without use of the system. The analysis of the 128 resulting scans showed higher quality of the images (quality score: +23%; P<.001), less images per scan (4.6 vs 6.3 [without the CDSS]; P<.001), and higher confidence in reporting conclusions (trust score: +20%; P<.001) with use of the system. Further, use of the system cost an additional 8 minutes per scan. We observed a correct diagnosis of pregnancy location in 39 (61%) and 52 (81%) of 64 scans in the nonassisted mode and assisted mode, respectively. Additionally, an exact diagnosis (with precise ectopic location) was made in 30 (47%) and 49 (73%) of the 64 scans without and with use of the system, respectively. These differences in diagnostic performance (+20% for correct location diagnosis and +30% for exact diagnosis) were both statistically significant (P=.002 and P<.001, respectively). CONCLUSIONS The Intelligent Scan Assistant System is based on an expert-validated knowledge base and demonstrates significant improvement in early pregnancy scanning, both in diagnostic performance (pregnancy location and precise diagnosis) and scan quality (selection of images, confidence, and image quality).