1
|
Han X, Yu J, Yang X, Chen C, Zhou H, Qiu C, Cao Y, Zhang T, Peng M, Zhu G, Ni D, Zhang Y, Liu N. Artificial intelligence assistance for fetal development: evaluation of an automated software for biometry measurements in the mid-trimester. BMC Pregnancy Childbirth 2024; 24:158. [PMID: 38395822 PMCID: PMC10885506 DOI: 10.1186/s12884-024-06336-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 02/08/2024] [Indexed: 02/25/2024] Open
Abstract
BACKGROUND This study presents CUPID, an advanced automated measurement software based on Artificial Intelligence (AI), designed to evaluate nine fetal biometric parameters in the mid-trimester. Our primary objective was to assess and compare the CUPID performance of experienced senior and junior radiologists. MATERIALS AND METHODS This prospective cross-sectional study was conducted at Shenzhen University General Hospital between September 2022 and June 2023, and focused on mid-trimester fetuses. All ultrasound images of the six standard planes, that enabled the evaluation of nine biometric measurements, were included to compare the performance of CUPID through subjective and objective assessments. RESULTS There were 642 fetuses with a mean (±SD) age of 22 ± 2.82 weeks at enrollment. In the subjective quality assessment, out of 642 images representing nine biometric measurements, 617-635 images (90.65-96.11%) of CUPID caliper placements were determined to be accurately placed and did not require any adjustments. Whereas, for the junior category, 447-691 images (69.63-92.06%) were determined to be accurately placed and did not require any adjustments. In the objective measurement indicators, across all nine biometric parameters and estimated fetal weight (EFW), the intra-class correlation coefficients (ICC) (0.843-0.990) and Pearson correlation coefficients (PCC) (0.765-0.978) between the senior radiologist and CUPID reflected good reliability compared with the ICC (0.306-0.937) and PCC (0.566-0.947) between the senior and junior radiologists. Additionally, the mean absolute error (MAE), percentage error (PE), and average error in days of gestation were lower between the senior and CUPID compared to the difference between the senior and junior radiologists. The specific differences are as follows: MAE (0.36-2.53 mm, 14.67 g) compared to (0.64- 8.13 mm, 38.05 g), PE (0.94-9.38%) compared to (1.58-16.04%), and average error in days (3.99-7.92 days) compared to (4.35-11.06 days). In the time-consuming task, CUPID only takes 0.05-0.07 s to measure nine biometric parameters, while senior and junior radiologists require 4.79-11.68 s and 4.95-13.44 s, respectively. CONCLUSIONS CUPID has proven to be highly accurate and efficient software for automatically measuring fetal biometry, gestational age, and fetal weight, providing a precise and fast tool for assessing fetal growth and development.
Collapse
Affiliation(s)
- Xuesong Han
- Department of Ultrasonography, Shenzhen University General Hospital, Shenzhen, Guangdong, China
| | - Junxuan Yu
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong, China
- Medical Ultrasound Image Computing (MUSIC) Lab, Shenzhen University, Shenzhen, Guangdong, China
- Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, Guangdong, China
| | - Xin Yang
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong, China
- Medical Ultrasound Image Computing (MUSIC) Lab, Shenzhen University, Shenzhen, Guangdong, China
- Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, Guangdong, China
| | - Chaoyu Chen
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong, China
- Medical Ultrasound Image Computing (MUSIC) Lab, Shenzhen University, Shenzhen, Guangdong, China
- Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, Guangdong, China
| | - Han Zhou
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong, China
- Medical Ultrasound Image Computing (MUSIC) Lab, Shenzhen University, Shenzhen, Guangdong, China
- Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, Guangdong, China
| | - Chuangxin Qiu
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong, China
- Medical Ultrasound Image Computing (MUSIC) Lab, Shenzhen University, Shenzhen, Guangdong, China
- Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, Guangdong, China
| | - Yan Cao
- Shenzhen RayShape Medical Technology Co., Ltd, Shenzhen, Guangdong, China
| | | | | | - Guiyao Zhu
- Department of Ultrasonography, Shenzhen University General Hospital, Shenzhen, Guangdong, China
| | - Dong Ni
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong, China
- Medical Ultrasound Image Computing (MUSIC) Lab, Shenzhen University, Shenzhen, Guangdong, China
- Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, Guangdong, China
| | - Yuanji Zhang
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong, China.
- Medical Ultrasound Image Computing (MUSIC) Lab, Shenzhen University, Shenzhen, Guangdong, China.
- Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, Guangdong, China.
| | - Nana Liu
- Department of Ultrasonography, Shenzhen University General Hospital, Shenzhen, Guangdong, China.
| |
Collapse
|
2
|
Donadono V, Ambroise Grandjean G, Stegen ML, Collin A, Bertholdt C, Casagrandi D, Morel O, Napolitano R. Training in Obstetric Ultrasound Biometry: Results from a Multicenter Reproducibility Study. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2022; 41:2819-2825. [PMID: 35302655 DOI: 10.1002/jum.15969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Revised: 02/04/2022] [Accepted: 02/12/2022] [Indexed: 06/14/2023]
Abstract
OBJECTIVES To assess the intra- and interobserver reproducibility of fetal biometry measurements obtained by trainee (junior) and experienced sonographers (senior) in the contest of two training programs in obstetric ultrasound. METHODS This was a prospective study on 192 women recruited ensuring an even distribution throughout gestation (18-41 weeks), at University College London Hospital (UCLH), England (87 cases), and at Maternité Regionale Universitaire de Nancy (MRUN), France (105 cases). The training took place in two training centers with experience in ultrasound training and subspecialist training in fetal medicine. Measurements for head circumference (HC), abdominal circumference (AC), and femur length (FL) were obtained twice by junior and senior sonographers, blind to their own and each other's measurements. Differences between and within sonographers were expressed in millimeters and as a percentage of fetal dimensions. Reproducibility was assessed using Bland-Altman plots. RESULTS Reproducibility was overall high with 95% confidence intervals (CI) within <6% for intraobserver and <8% for interobserver reproducibility. Intraobserver reproducibility was lower within junior than within senior sonographers' measurements for HC (95% CI: <4% versus <3%) and FL (95% CI: <6% and < 5%). Intraobserver reproducibility was similar between the two centers/training programs (AC 95% CI: <6%). Cumulative interobserver reproducibility in both centers was similar to the reproducibility within a single site (95% CI: <5%, <8%, and <7% for HC, AC, and FL, respectively). CONCLUSIONS Reproducibility of fetal biometry measurement was high in centers with structured training programs regardless of sonographers' experience. Reproducibility was higher in sonographers who completed the training.
Collapse
Affiliation(s)
- Vera Donadono
- Fetal Medicine Unit, University College London Hospitals NHS Foundation Trust, London, England
| | - Gaëlle Ambroise Grandjean
- Inserm, IADI, Université de Lorraine, Nancy, France
- Midwifery Department, Université de Lorraine, Nancy, France
- Obstetrics Department, CHRU Nancy, Nancy, France
| | - Marie-Louise Stegen
- Fetal Medicine Unit, University College London Hospitals NHS Foundation Trust, London, England
| | | | - Charline Bertholdt
- Inserm, IADI, Université de Lorraine, Nancy, France
- Obstetrics Department, CHRU Nancy, Nancy, France
| | - Davide Casagrandi
- Fetal Medicine Unit, University College London Hospitals NHS Foundation Trust, London, England
- Elizabeth Garrett Anderson Wing, Institute for Women's Health, University College London, London, England
| | - Olivier Morel
- Inserm, IADI, Université de Lorraine, Nancy, France
- Obstetrics Department, CHRU Nancy, Nancy, France
| | - Raffaele Napolitano
- Fetal Medicine Unit, University College London Hospitals NHS Foundation Trust, London, England
- Elizabeth Garrett Anderson Wing, Institute for Women's Health, University College London, London, England
| |
Collapse
|
4
|
Jacques T, Fournier L, Zins M, Adamsbaum C, Chaumoitre K, Feydy A, Millet I, Montaudon M, Beregi JP, Bartoli JM, Cart P, Masson JP, Meder JF, Boyer L, Cotten A. Proposals for the use of artificial intelligence in emergency radiology. Diagn Interv Imaging 2020; 102:63-68. [PMID: 33279461 DOI: 10.1016/j.diii.2020.11.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Accepted: 11/13/2020] [Indexed: 12/30/2022]
Affiliation(s)
- Thibaut Jacques
- Department of Musculoskeletal Imaging, Lille University Hospital, 59000 Lille, France; Lille University School of Medicine, 59000 Lille, France.
| | - Laure Fournier
- Inserm, PARCC, 75015 Paris, France; Université de Paris, 75006 Paris, France; Radiology Department, Hôpital Européen Georges Pompidou, AP-HP, 75015 Paris, France; DRIM France IA, 75013 Paris, France; Collège des Enseignants en Radiologie de France (CERF), 75013 Paris, France
| | - Marc Zins
- DRIM France IA, 75013 Paris, France; Department of Medical Imaging, Saint-Joseph Hospital, 75014 Paris, France
| | - Catherine Adamsbaum
- Faculty of Medicine, Paris-Saclay University, 94270 Le-Kremlin-Bicêtre, France; Pediatric Radiology Department, Bicêtre Hospital, AP-HP, 94270 Le-Kremlin-Bicêtre, France
| | - Kathia Chaumoitre
- Imaging Department, Hôpital Nord, APHM, 13015 Marseille, France; Aix-Marseille University, 13007 Marseille, France
| | - Antoine Feydy
- Department of Radiology B, Cochin Hospital, AP-HP, 75014 Paris, France; Université de Paris, 75006 Paris, France
| | - Ingrid Millet
- Department of Medical Imaging, Lapeyronie University Hospital, 34295 Montpellier, France; Inserm, UMR, Institut Desbrest d'Épidémiologie et de Santé publique, University of Montpellier, 34000 Montpellier, France
| | - Michel Montaudon
- Collège des Enseignants en Radiologie de France (CERF), 75013 Paris, France; Department of Cardiovascular Imaging, Hôpital Cardiologique du Haut-Lévêque, CHU de Bordeaux, 33600 Pessac, France; Inserm U1045, IHU LIRYC, Université de Bordeaux, 33600 Pessac, France
| | - Jean-Paul Beregi
- DRIM France IA, 75013 Paris, France; Collège des Enseignants en Radiologie de France (CERF), 75013 Paris, France; Medical Imaging Group Nîmes, Nîmes University Hospital, 34000 Nîmes, France
| | - Jean-Michel Bartoli
- DRIM France IA, 75013 Paris, France; Collège des Enseignants en Radiologie de France (CERF), 75013 Paris, France; Radiology, La Timone Hospital, 13000 Marseille, France
| | - Philippe Cart
- Groupement Hospitalier Intercommunal Nord Ardennes, 08000 Charleville-Mézières, France; Syndicat des Radiologues Hospitaliers, 75004 Paris, France
| | - Jean-Philippe Masson
- DRIM France IA, 75013 Paris, France; Fédération Nationale des Médecins Radiologues, 75007 Paris, France
| | - Jean-François Meder
- Université de Paris, 75006 Paris, France; Department of Neuroradiology, Sainte-Anne Hospital, 75014 Paris, France; Inserm UMR 894, Faculty of Medicine, Pyschiatry and Neurosciences Centers, Paris Descartes University, Sorbonne Paris Cité, 75014 Paris, France; Société Française de Radiologie, 75013 Paris, France
| | - Louis Boyer
- Department of Radiology, Hôpital Montpied, CHU de Clermont-Ferrand, 63000 Clermont-Ferrand, France; TGI, Institut Pascal UMR 6602 UCA/CNRS/SIGMA Clermont, 63000 Clermont-Ferrand, France; Conseil National Professionnel de Radiologie (G4), 75013 Paris, France
| | - Anne Cotten
- Department of Musculoskeletal Imaging, Lille University Hospital, 59000 Lille, France; Lille University School of Medicine, 59000 Lille, France; Collège des Enseignants en Radiologie de France (CERF), 75013 Paris, France; Société Française de Radiologie, 75013 Paris, France
| |
Collapse
|