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Medjedovic E, Stanojevic M, Jonuzovic-Prosic S, Ribic E, Begic Z, Cerovac A, Badnjevic A. Artificial intelligence as a new answer to old challenges in maternal-fetal medicine and obstetrics. Technol Health Care 2024; 32:1273-1287. [PMID: 38073356 DOI: 10.3233/thc-231482] [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] [Indexed: 05/12/2024]
Abstract
BACKGROUND Following the latest trends in the development of artificial intelligence (AI), the possibility of processing an immense amount of data has created a breakthrough in the medical field. Practitioners can now utilize AI tools to advance diagnostic protocols and improve patient care. OBJECTIVE The aim of this article is to present the importance and modalities of AI in maternal-fetal medicine and obstetrics and its usefulness in daily clinical work and decision-making process. METHODS A comprehensive literature review was performed by searching PubMed for articles published from inception up until August 2023, including the search terms "artificial intelligence in obstetrics", "maternal-fetal medicine", and "machine learning" combined through Boolean operators. In addition, references lists of identified articles were further reviewed for inclusion. RESULTS According to recent research, AI has demonstrated remarkable potential in improving the accuracy and timeliness of diagnoses in maternal-fetal medicine and obstetrics, e.g., advancing perinatal ultrasound technique, monitoring fetal heart rate during labor, or predicting mode of delivery. The combination of AI and obstetric ultrasound can help optimize fetal ultrasound assessment by reducing examination time and improving diagnostic accuracy while reducing physician workload. CONCLUSION The integration of AI in maternal-fetal medicine and obstetrics has the potential to significantly improve patient outcomes, enhance healthcare efficiency, and individualized care plans. As technology evolves, AI algorithms are likely to become even more sophisticated. However, the successful implementation of AI in maternal-fetal medicine and obstetrics needs to address challenges related to interpretability and reliability.
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Affiliation(s)
- Edin Medjedovic
- Clinic of Gynecology and Obstetrics, Clinical Center University of Sarajevo, Sarajevo, Bosnia and Herzegovina
- Department of Gynecology, Obstetrics and Reproductive Medicine, School of Medicine, Sarajevo School of Science and Technology, Sarajevo, Bosnia and Herzegovina
| | - Milan Stanojevic
- Department of Obstetrics and Gynecology, University Hospital "Sveti Duh", Zagreb, Croatia
| | - Sabaheta Jonuzovic-Prosic
- Clinic of Gynecology and Obstetrics, Clinical Center University of Sarajevo, Sarajevo, Bosnia and Herzegovina
| | - Emina Ribic
- Public Institution Department for Health Care of Women and Maternity of Sarajevo Canton, Sarajevo, Bosnia and Herzegovina
| | - Zijo Begic
- Department of Cardiology, Pediatric Clinic, Clinical Center University of Sarajevo, Sarajevo, Bosnia and Herzegovina
| | - Anis Cerovac
- Department of Gynecology and Obstetrics Tesanj, General Hospital Tesanj, Bosnia and Herzegovina
| | - Almir Badnjevic
- International Burch University, Sarajevo, Bosnia and Herzegovina
- Genetics and Bioengineering Department, Faculty of Engineering and Natural Sciences, Sarajevo, Bosnia and Herzegovina
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Sivajohan B, Elgendi M, Menon C, Allaire C, Yong P, Bedaiwy MA. Clinical use of artificial intelligence in endometriosis: a scoping review. NPJ Digit Med 2022; 5:109. [PMID: 35927426 PMCID: PMC9352729 DOI: 10.1038/s41746-022-00638-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 06/24/2022] [Indexed: 02/07/2023] Open
Abstract
Endometriosis is a chronic, debilitating, gynecologic condition with a non-specific clinical presentation. Globally, patients can experience diagnostic delays of ~6 to 12 years, which significantly hinders adequate management and places a significant financial burden on patients and the healthcare system. Through artificial intelligence (AI), it is possible to create models that can extract data patterns to act as inputs for developing interventions with predictive and diagnostic accuracies that are superior to conventional methods and current tools used in standards of care. This literature review explored the use of AI methods to address different clinical problems in endometriosis. Approximately 1309 unique records were found across four databases; among those, 36 studies met the inclusion criteria. Studies were eligible if they involved an AI approach or model to explore endometriosis pathology, diagnostics, prediction, or management and if they reported evaluation metrics (sensitivity and specificity) after validating their models. Only articles accessible in English were included in this review. Logistic regression was the most popular machine learning method, followed by decision tree algorithms, random forest, and support vector machines. Approximately 44.4% (n = 16) of the studies analyzed the predictive capabilities of AI approaches in patients with endometriosis, while 47.2% (n = 17) explored diagnostic capabilities, and 8.33% (n = 3) used AI to improve disease understanding. Models were built using different data types, including biomarkers, clinical variables, metabolite spectra, genetic variables, imaging data, mixed methods, and lesion characteristics. Regardless of the AI-based endometriosis application (either diagnostic or predictive), pooled sensitivities ranged from 81.7 to 96.7%, and pooled specificities ranged between 70.7 and 91.6%. Overall, AI models displayed good diagnostic and predictive capacity in detecting endometriosis using simple classification scenarios (i.e., differentiating between cases and controls), showing promising directions for AI in assessing endometriosis in the near future. This timely review highlighted an emerging area of interest in endometriosis and AI. It also provided recommendations for future research in this field to improve the reproducibility of results and comparability between models, and further test the capacity of these models to enhance diagnosis, prediction, and management in endometriosis patients.
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Affiliation(s)
- Brintha Sivajohan
- Schulich School of Medicine & Dentistry, Western University, London, ON, Canada.,Department of Obstetrics and Gynecology, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Mohamed Elgendi
- Department of Obstetrics and Gynecology, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada.,Biomedical and Mobile Health Technology Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Carlo Menon
- Biomedical and Mobile Health Technology Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Catherine Allaire
- Department of Obstetrics and Gynecology, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada.,British Columbia Women's Hospital, Vancouver, BC, Canada
| | - Paul Yong
- Department of Obstetrics and Gynecology, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada.,British Columbia Women's Hospital, Vancouver, BC, Canada
| | - Mohamed A Bedaiwy
- Department of Obstetrics and Gynecology, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada. .,British Columbia Women's Hospital, Vancouver, BC, Canada.
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Çitil ET, Çitil Canbay F. Artificial intelligence and the future of midwifery: What do midwives think about artificial intelligence? A qualitative study. Health Care Women Int 2022; 43:1510-1527. [PMID: 35452353 DOI: 10.1080/07399332.2022.2055760] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
The evidence on how AI will make a revolution is insufficient. Our aim was to investigate opinions of midwives on the future of AI and midwifery. Semi-structured interviews were done with 18 midwives in Turkey. Themes were identified: expectations included the advantages and conditional acceptance of robotic technology, prejudices reflected perceived shortcomings, lack of human competencies, and trust issues. Concerns included midwifery care and concerns about her future. Midwives were overwhelmingly skeptical about the replacement of human capabilities by AI and found the technology's potential limited.
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Affiliation(s)
- Elif Tuğçe Çitil
- Department of Midwifery, Health Science Faculty, Kütahya Health Science University, Kütahya, Turkey
| | - Funda Çitil Canbay
- Department of Midwifery, Health Science Faculty, Atatürk University, Erzurum, Turkey
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