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Ferreira I, Simões J, Pereira B, Correia J, Areia AL. Ensemble learning for fetal ultrasound and maternal-fetal data to predict mode of delivery after labor induction. Sci Rep 2024; 14:15275. [PMID: 38961231 PMCID: PMC11222528 DOI: 10.1038/s41598-024-65394-6] [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: 01/09/2024] [Accepted: 06/19/2024] [Indexed: 07/05/2024] Open
Abstract
Providing adequate counseling on mode of delivery after induction of labor (IOL) is of utmost importance. Various AI algorithms have been developed for this purpose, but rely on maternal-fetal data, not including ultrasound (US) imaging. We used retrospectively collected clinical data from 808 subjects submitted to IOL, totaling 2024 US images, to train AI models to predict vaginal delivery (VD) and cesarean section (CS) outcomes after IOL. The best overall model used only clinical data (F1-score: 0.736; positive predictive value (PPV): 0.734). The imaging models employed fetal head, abdomen and femur US images, showing limited discriminative results. The best model used femur images (F1-score: 0.594; PPV: 0.580). Consequently, we constructed ensemble models to test whether US imaging could enhance the clinical data model. The best ensemble model included clinical data and US femur images (F1-score: 0.689; PPV: 0.693), presenting a false positive and false negative interesting trade-off. The model accurately predicted CS on 4 additional cases, despite misclassifying 20 additional VD, resulting in a 6.0% decrease in average accuracy compared to the clinical data model. Hence, integrating US imaging into the latter model can be a new development in assisting mode of delivery counseling.
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Affiliation(s)
- Iolanda Ferreira
- Faculty of Medicine of University of Coimbra, Obstetrics Department, University and Hospitalar Centre of Coimbra, Coimbra, Portugal.
- Maternidade Doutor Daniel de Matos, R. Miguel Torga, 3030-165, Coimbra, Portugal.
| | - Joana Simões
- Department of Informatics Engineering, Centre for Informatics and Systems of the University of Coimbra, University of Coimbra, Coimbra, Portugal
| | - Beatriz Pereira
- Department of Physics, University of Coimbra, Coimbra, Portugal
| | - João Correia
- Department of Informatics Engineering, Centre for Informatics and Systems of the University of Coimbra, University of Coimbra, Coimbra, Portugal
| | - Ana Luísa Areia
- Faculty of Medicine of University of Coimbra, Obstetrics Department, University and Hospitalar Centre of Coimbra, Coimbra, Portugal
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Slimani S, Hounka S, Mahmoudi A, Rehah T, Laoudiyi D, Saadi H, Bouziyane A, Lamrissi A, Jalal M, Bouhya S, Akiki M, Bouyakhf Y, Badaoui B, Radgui A, Mhlanga M, Bouyakhf EH. Fetal biometry and amniotic fluid volume assessment end-to-end automation using Deep Learning. Nat Commun 2023; 14:7047. [PMID: 37923713 PMCID: PMC10624828 DOI: 10.1038/s41467-023-42438-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 10/10/2023] [Indexed: 11/06/2023] Open
Abstract
Fetal biometry and amniotic fluid volume assessments are two essential yet repetitive tasks in fetal ultrasound screening scans, aiding in the detection of potentially life-threatening conditions. However, these assessment methods can occasionally yield unreliable results. Advances in deep learning have opened up new avenues for automated measurements in fetal ultrasound, demonstrating human-level performance in various fetal ultrasound tasks. Nevertheless, the majority of these studies are retrospective in silico studies, with a limited number including African patients in their datasets. In this study we developed and prospectively assessed the performance of deep learning models for end-to-end automation of fetal biometry and amniotic fluid volume measurements. These models were trained using a newly constructed database of 172,293 de-identified Moroccan fetal ultrasound images, supplemented with publicly available datasets. the models were then tested on prospectively acquired video clips from 172 pregnant people forming a consecutive series gathered at four healthcare centers in Morocco. Our results demonstrate that the 95% limits of agreement between the models and practitioners for the studied measurements were narrower than the reported intra- and inter-observer variability among expert human sonographers for all the parameters under study. This means that these models could be deployed in clinical conditions, to alleviate time-consuming, repetitive tasks, and make fetal ultrasound more accessible in limited-resource environments.
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Affiliation(s)
- Saad Slimani
- Deepecho, 10106, Rabat, Morocco.
- Ibn Rochd University Hospital, Hassan II University, 20100, Casablanca, Morocco.
| | - Salaheddine Hounka
- Telecommunications Systems Services and Networks lab (STRS Lab), INPT, 10112, Rabat, Morocco
| | - Abdelhak Mahmoudi
- Deepecho, 10106, Rabat, Morocco
- Ecole Normale Supérieure, LIMIARF, Mohammed V University in Rabat, 4014, Rabat, Morocco
| | | | - Dalal Laoudiyi
- Ibn Rochd University Hospital, Hassan II University, 20100, Casablanca, Morocco
| | - Hanane Saadi
- Mohammed VI University Hospital, 60049, Oujda, Morocco
| | - Amal Bouziyane
- Université Mohammed VI des Sciences de la Santé, Hôpital Universitaire Cheikh Khalifa, 82403, Casablanca, Morocco
| | - Amine Lamrissi
- Ibn Rochd University Hospital, Hassan II University, 20100, Casablanca, Morocco
| | - Mohamed Jalal
- Ibn Rochd University Hospital, Hassan II University, 20100, Casablanca, Morocco
| | - Said Bouhya
- Ibn Rochd University Hospital, Hassan II University, 20100, Casablanca, Morocco
| | | | | | - Bouabid Badaoui
- Laboratory of Biodiversity, Ecology, and Genome, Department of Biology, Faculty of Sciences, Mohammed V University in Rabat, 1014, Rabat, Morocco
- African Sustainable Agriculture Research Institute (ASARI), Mohammed VI Polytechnic University (UM6P), 43150, Laâyoune, Morocco
| | - Amina Radgui
- Telecommunications Systems Services and Networks lab (STRS Lab), INPT, 10112, Rabat, Morocco
| | - Musa Mhlanga
- Radboud Institute for Molecular Life Sciences, Epigenomics & Single Cell Biophysics, 6525 XZ, Nijmegen, the Netherlands
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Horgan R, Nehme L, Abuhamad A. Artificial intelligence in obstetric ultrasound: A scoping review. Prenat Diagn 2023; 43:1176-1219. [PMID: 37503802 DOI: 10.1002/pd.6411] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 06/05/2023] [Accepted: 07/17/2023] [Indexed: 07/29/2023]
Abstract
The objective is to summarize the current use of artificial intelligence (AI) in obstetric ultrasound. PubMed, Cochrane Library, and ClinicalTrials.gov databases were searched using the following keywords "neural networks", OR "artificial intelligence", OR "machine learning", OR "deep learning", AND "obstetrics", OR "obstetrical", OR "fetus", OR "foetus", OR "fetal", OR "foetal", OR "pregnancy", or "pregnant", AND "ultrasound" from inception through May 2022. The search was limited to the English language. Studies were eligible for inclusion if they described the use of AI in obstetric ultrasound. Obstetric ultrasound was defined as the process of obtaining ultrasound images of a fetus, amniotic fluid, or placenta. AI was defined as the use of neural networks, machine learning, or deep learning methods. The authors' search identified a total of 127 papers that fulfilled our inclusion criteria. The current uses of AI in obstetric ultrasound include first trimester pregnancy ultrasound, assessment of placenta, fetal biometry, fetal echocardiography, fetal neurosonography, assessment of fetal anatomy, and other uses including assessment of fetal lung maturity and screening for risk of adverse pregnancy outcomes. AI holds the potential to improve the ultrasound efficiency, pregnancy outcomes in low resource settings, detection of congenital malformations and prediction of adverse pregnancy outcomes.
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Affiliation(s)
- Rebecca Horgan
- Division of Maternal Fetal Medicine, Department of Obstetrics & Gynecology, Eastern Virginia Medical School, Norfolk, Virginia, USA
| | - Lea Nehme
- Division of Maternal Fetal Medicine, Department of Obstetrics & Gynecology, Eastern Virginia Medical School, Norfolk, Virginia, USA
| | - Alfred Abuhamad
- Division of Maternal Fetal Medicine, Department of Obstetrics & Gynecology, Eastern Virginia Medical School, Norfolk, Virginia, USA
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Walker MC, Willner I, Miguel OX, Murphy MSQ, El-Chaâr D, Moretti F, Dingwall Harvey ALJ, Rennicks White R, Muldoon KA, Carrington AM, Hawken S, Aviv RI. Using deep-learning in fetal ultrasound analysis for diagnosis of cystic hygroma in the first trimester. PLoS One 2022; 17:e0269323. [PMID: 35731736 PMCID: PMC9216531 DOI: 10.1371/journal.pone.0269323] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Accepted: 05/19/2022] [Indexed: 11/30/2022] Open
Abstract
Objective To develop and internally validate a deep-learning algorithm from fetal ultrasound images for the diagnosis of cystic hygromas in the first trimester. Methods All first trimester ultrasound scans with a diagnosis of a cystic hygroma between 11 and 14 weeks gestation at our tertiary care centre in Ontario, Canada were studied. Ultrasound scans with normal nuchal translucency were used as controls. The dataset was partitioned with 75% of images used for model training and 25% used for model validation. Images were analyzed using a DenseNet model and the accuracy of the trained model to correctly identify cases of cystic hygroma was assessed by calculating sensitivity, specificity, and the area under the receiver-operating characteristic (ROC) curve. Gradient class activation heat maps (Grad-CAM) were generated to assess model interpretability. Results The dataset included 289 sagittal fetal ultrasound images;129 cystic hygroma cases and 160 normal NT controls. Overall model accuracy was 93% (95% CI: 88–98%), sensitivity 92% (95% CI: 79–100%), specificity 94% (95% CI: 91–96%), and the area under the ROC curve 0.94 (95% CI: 0.89–1.0). Grad-CAM heat maps demonstrated that the model predictions were driven primarily by the fetal posterior cervical area. Conclusions Our findings demonstrate that deep-learning algorithms can achieve high accuracy in diagnostic interpretation of cystic hygroma in the first trimester, validated against expert clinical assessment.
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Affiliation(s)
- Mark C. Walker
- Department of Obstetrics and Gynecology, University of Ottawa, Ottawa, Canada
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
- International and Global Health Office, University of Ottawa, Ottawa, Canada
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada
- Department of Obstetrics, Gynecology & Newborn Care, The Ottawa Hospital, Ottawa, Canada
- BORN Ontario, Children’s Hospital of Eastern Ontario Research Institute, Ottawa, Canada
- * E-mail:
| | - Inbal Willner
- Department of Obstetrics and Gynecology, University of Ottawa, Ottawa, Canada
- Department of Obstetrics, Gynecology & Newborn Care, The Ottawa Hospital, Ottawa, Canada
| | - Olivier X. Miguel
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
| | - Malia S. Q. Murphy
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
| | - Darine El-Chaâr
- Department of Obstetrics and Gynecology, University of Ottawa, Ottawa, Canada
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada
- Department of Obstetrics, Gynecology & Newborn Care, The Ottawa Hospital, Ottawa, Canada
| | - Felipe Moretti
- Department of Obstetrics and Gynecology, University of Ottawa, Ottawa, Canada
- Department of Obstetrics, Gynecology & Newborn Care, The Ottawa Hospital, Ottawa, Canada
| | | | - Ruth Rennicks White
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
- Department of Obstetrics, Gynecology & Newborn Care, The Ottawa Hospital, Ottawa, Canada
| | - Katherine A. Muldoon
- Department of Obstetrics and Gynecology, University of Ottawa, Ottawa, Canada
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
| | - André M. Carrington
- Department of Systems Design Engineering, University of Waterloo, Waterloo, Canada
- Department of Radiology and Medical Imaging, University of Ottawa, Ottawa, Canada
| | - Steven Hawken
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada
| | - Richard I. Aviv
- Department of Radiology and Medical Imaging, University of Ottawa, Ottawa, Canada
- Department of Radiology and Medical Imaging, The Ottawa Hospital, Ottawa, Canada
- Neuroscience Program, Ottawa Hospital Research Institute, Ottawa, Canada
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