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J Med Internet Res. 2019 Jul 3;21(7):e14286. doi: 10.2196/14286.

A Novel Intelligent Scan Assistant System for Early Pregnancy Diagnosis by Ultrasound: Clinical Decision Support System Evaluation Study.

Author information

1
Service de Médecine Fœtale, Sorbonne Université, Assistance Publique - Hôpitaux de Paris / Hôpitaux Universitaires Est Parisiens, Hôpital Armand Trousseau, Paris, France.
2
Medical Informatics and Knowledge Engineering for eHealth Lab, INSERM, Paris, France.
3
Direction de la Recherche et de l'Innovation, Assistance Publique - Hôpitaux de Paris, Paris, France.
4
Service de Radiologie, Sorbonne Université, Assistance Publique - Hôpitaux de Paris / Hôpitaux Universitaires Est Parisiens, Hôpital Armand Trousseau, Paris, France.
5
Obstetrical, Perinatal and Pediatric Epidemiology Research Team, Center for Biostatistics and Epidemiology, INSERM, Paris, France.
6
Gynaecology Diagnostic and Outpatient Treatment Unit, University College Hospital and Institute for Women's Health, University College London, London, United Kingdom.

Abstract

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).

KEYWORDS:

decision support system; ectopic pregnancy; knowledge base; medical ultrasound; ontology

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