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Sinha R, Raina R, Bag M, Rupa B. Empowering gynaecologists with Artificial Intelligence: Tailoring surgical solutions for fibroids. Eur J Obstet Gynecol Reprod Biol 2024; 299:72-77. [PMID: 38838389 DOI: 10.1016/j.ejogrb.2024.06.001] [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: 01/10/2024] [Revised: 05/29/2024] [Accepted: 06/02/2024] [Indexed: 06/07/2024]
Abstract
BACKGROUND In recent years, the integration ofArtificial intelligence (AI) into various fields of medicine including Gynaecology, has shown promising potential. Surgical treatment of fibroid is myomectomy if uterine preservation and fertility are the primary aims. AI usage begins with the involvement of LLM (Large Language Model) from the point when a patient visits a gynecologist, from identifying signs and symptoms to reaching a diagnosis, providing treatment plans, and patient counseling. OBJECTIVE Use of AI (ChatGPT versus Google Bard) in the surgical management of fibroid. STUDY DESIGN Identifyingthe patient's problems using LLMs like ChatGPT and Google Bard and giving a treatment optionin 8 clinical scenarios of fibroid. Data entry was done using M.S. Excel and was statistically analyzed using Statistical Package for Social Sciences (SPSS Version 26) for M.S. Windows 2010. All results were presented in tabular form. Data were analyzed using nonparametric tests Chi-square tests or Fisher exact test.pvalues < 0.05 were considered statistically significant. The sensitivity of both techniques was calculated. We have used Cohen's Kappa to know the degree of agreement. RESULTS We found that on the first attempt, ChatGPT gave general answers in 62.5 % of cases and specific answers in 37.5 % of cases. ChatGPT showed improved sensitivity on successive prompts 37.5 % to 62.5 % on the third prompt. Google Bard could not identify the clinical question in 50 % of cases and gave incorrect answers in 12.5 % of cases (p = 0.04). Google Bard showed the same sensitivity of 25 % on all prompts. CONCLUSION AI helps to reduce the time to diagnose and plan a treatment strategy for fibroid and acts as a powerful tool in the hands of a gynecologist. However, the usage of AI by patients for self-treatment is to be avoided and should be used only for education and counseling about fibroids.
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Affiliation(s)
- Rooma Sinha
- Department of Obstetrics and Gynaecology, Apollo Health City, Jubilee Hills, Hyderabad 500033, India
| | - Rohit Raina
- Department of Obstetrics and Gynaecology, Apollo Health City, Jubilee Hills, Hyderabad 500033, India.
| | - Moumita Bag
- Department of Obstetrics and Gynaecology, Apollo Health City, Jubilee Hills, Hyderabad 500033, India
| | - Bana Rupa
- Department of Obstetrics and Gynaecology, Apollo Health City, Jubilee Hills, Hyderabad 500033, India
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Haj Yahya R, Roman A, Grant S, Whitehead CL. Antenatal screening for fetal structural anomalies - Routine or targeted practice? Best Pract Res Clin Obstet Gynaecol 2024:102521. [PMID: 38997900 DOI: 10.1016/j.bpobgyn.2024.102521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 05/29/2024] [Accepted: 06/12/2024] [Indexed: 07/14/2024]
Abstract
Antenatal screening with ultrasound identifies fetal structural anomalies in 3-6% of pregnancies. Identification of anomalies during pregnancy provides an opportunity for counselling, targeted imaging, genetic testing, fetal intervention and delivery planning. Ultrasound is the primary modality for imaging the fetus in pregnancy, but magnetic resonance imaging (MRI) is evolving as an adjunctive tool providing additional structural and functional information. Screening should start from the first trimester when more than 50% of severe defects can be detected. The mid-trimester ultrasound balances the benefits of increased fetal growth and development to improve detection rates, whilst still providing timely management options. A routine third trimester ultrasound may detect acquired anomalies or those missed earlier in pregnancy but may not be available in all settings. Targeted imaging by fetal medicine experts improves detection in high-risk pregnancies or when an anomaly has been detected, allowing accurate phenotyping, access to advanced genetic testing and expert counselling.
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Affiliation(s)
- Rani Haj Yahya
- Department of Fetal Medicine, The Royal Women's Hospital, Parkville, Australia; Perinatal Research Group, Dept. Obstetrics, Gynaecology, Newborn, University of Melbourne, Parkville, Australia.
| | - Alina Roman
- Department of Fetal Medicine, The Royal Women's Hospital, Parkville, Australia.
| | - Steven Grant
- Department of Fetal Medicine, The Royal Women's Hospital, Parkville, Australia.
| | - Clare L Whitehead
- Department of Fetal Medicine, The Royal Women's Hospital, Parkville, Australia; Perinatal Research Group, Dept. Obstetrics, Gynaecology, Newborn, University of Melbourne, Parkville, Australia.
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Yaseen I, Rather RA. A Theoretical Exploration of Artificial Intelligence's Impact on Feto-Maternal Health from Conception to Delivery. Int J Womens Health 2024; 16:903-915. [PMID: 38800118 PMCID: PMC11128252 DOI: 10.2147/ijwh.s454127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Accepted: 04/29/2024] [Indexed: 05/29/2024] Open
Abstract
The implementation of Artificial Intelligence (AI) in healthcare is enhancing diagnostic accuracy in clinical setups. The use of AI in healthcare is steadily increasing with advancing technology, extending beyond disease diagnosis to encompass roles in feto-maternal health. AI harnesses Machine Learning (ML), Natural Language Processing (NLP), Artificial Neural Networks (ANN), and computer vision to analyze data and draw conclusions. Considering maternal health, ML analyzes vast datasets to predict maternal and fetal health outcomes, while NLP interprets medical texts and patient records to assist in diagnosis and treatment decisions. ANN models identify patterns in complex feto-maternal medical data, aiding in risk assessment and intervention planning whereas, computer vision enables the analysis of medical images for early detection of feto-maternal complications. AI facilitates early pregnancy detection, genetic screening, and continuous monitoring of maternal health parameters, providing real-time alerts for deviations, while also playing a crucial role in the early detection of fetal abnormalities through enhanced ultrasound imaging, contributing to informed decision-making. This review investigates into the application of AI, particularly through predictive models, in addressing the monitoring of feto-maternal health. Additionally, it examines potential future directions and challenges associated with these applications.
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Affiliation(s)
- Ishfaq Yaseen
- Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | - Riyaz Ahmad Rather
- Department of Biotechnology, College of Natural and Computational Science, Wachemo University, Hossana, Ethiopia
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Wu Z, Yu X, Wang F, Xu C. Application of artificial intelligence in dental implant prognosis: A scoping review. J Dent 2024; 144:104924. [PMID: 38467177 DOI: 10.1016/j.jdent.2024.104924] [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: 11/05/2023] [Revised: 02/19/2024] [Accepted: 03/03/2024] [Indexed: 03/13/2024] Open
Abstract
OBJECTIVES The purpose of this scoping review was to evaluate the performance of artificial intelligence (AI) in the prognosis of dental implants. DATA Studies that analyzed the performance of AI models in the prediction of implant prognosis based on medical records or radiographic images. Quality assessment was conducted using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Quasi-Experimental Studies. SOURCES This scoping review included studies published in English up to October 2023 in MEDLINE/PubMed, Embase, Cochrane Library, and Scopus. A manual search was also performed. STUDY SELECTION Of 892 studies, full-text analysis was conducted in 36 studies. Twelve studies met the inclusion criteria. Eight used deep learning models, 3 applied traditional machine learning algorithms, and 1 study combined both types. The performance was quantified using accuracy, sensitivity, specificity, precision, F1 score, and receiver operating characteristic area under curves (ROC AUC). The prognostic accuracy was analyzed and ranged from 70 % to 96.13 %. CONCLUSIONS AI is a promising tool in evaluating implant prognosis, but further enhancements are required. Additional radiographic and clinical data are needed to improve AI performance in implant prognosis. CLINICAL SIGNIFICANCE AI can predict the prognosis of dental implants based on radiographic images or medical records. As a result, clinicians can receive predicted implant prognosis with the assistance of AI before implant placement and make informed decisions.
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Affiliation(s)
- Ziang Wu
- Department of Prosthodontics, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; College of Stomatology, Shanghai Jiao Tong University, Shanghai, China; National Center for Stomatology, Shanghai, China; National Clinical Research Center for Oral Diseases, Shanghai, China; Shanghai Key Laboratory of Stomatology, Shanghai, China; Shanghai Research Institute of Stomatology, Shanghai, China
| | - Xinbo Yu
- College of Stomatology, Shanghai Jiao Tong University, Shanghai, China; National Center for Stomatology, Shanghai, China; National Clinical Research Center for Oral Diseases, Shanghai, China; Shanghai Key Laboratory of Stomatology, Shanghai, China; Shanghai Research Institute of Stomatology, Shanghai, China; Second Dental Center, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Feng Wang
- College of Stomatology, Shanghai Jiao Tong University, Shanghai, China; National Center for Stomatology, Shanghai, China; National Clinical Research Center for Oral Diseases, Shanghai, China; Shanghai Key Laboratory of Stomatology, Shanghai, China; Shanghai Research Institute of Stomatology, Shanghai, China; Second Dental Center, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Chun Xu
- Department of Prosthodontics, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; College of Stomatology, Shanghai Jiao Tong University, Shanghai, China; National Center for Stomatology, Shanghai, China; National Clinical Research Center for Oral Diseases, Shanghai, China; Shanghai Key Laboratory of Stomatology, Shanghai, China; Shanghai Research Institute of Stomatology, Shanghai, China.
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Shara N, Mirabal-Beltran R, Talmadge B, Falah N, Ahmad M, Dempers R, Crovatt S, Eisenberg S, Anderson K. Use of Machine Learning for Early Detection of Maternal Cardiovascular Conditions: Retrospective Study Using Electronic Health Record Data. JMIR Cardio 2024; 8:e53091. [PMID: 38648629 DOI: 10.2196/53091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 03/08/2024] [Accepted: 03/11/2024] [Indexed: 04/25/2024] Open
Abstract
BACKGROUND Cardiovascular conditions (eg, cardiac and coronary conditions, hypertensive disorders of pregnancy, and cardiomyopathies) were the leading cause of maternal mortality between 2017 and 2019. The United States has the highest maternal mortality rate of any high-income nation, disproportionately impacting those who identify as non-Hispanic Black or Hispanic. Novel clinical approaches to the detection and diagnosis of cardiovascular conditions are therefore imperative. Emerging research is demonstrating that machine learning (ML) is a promising tool for detecting patients at increased risk for hypertensive disorders during pregnancy. However, additional studies are required to determine how integrating ML and big data, such as electronic health records (EHRs), can improve the identification of obstetric patients at higher risk of cardiovascular conditions. OBJECTIVE This study aimed to evaluate the capability and timing of a proprietary ML algorithm, Healthy Outcomes for all Pregnancy Experiences-Cardiovascular-Risk Assessment Technology (HOPE-CAT), to detect maternal-related cardiovascular conditions and outcomes. METHODS Retrospective data from the EHRs of a large health care system were investigated by HOPE-CAT in a virtual server environment. Deidentification of EHR data and standardization enabled HOPE-CAT to analyze data without pre-existing biases. The ML algorithm assessed risk factors selected by clinical experts in cardio-obstetrics, and the algorithm was iteratively trained using relevant literature and current standards of risk identification. After refinement of the algorithm's learned risk factors, risk profiles were generated for every patient including a designation of standard versus high risk. The profiles were individually paired with clinical outcomes pertaining to cardiovascular pregnancy conditions and complications, wherein a delta was calculated between the date of the risk profile and the actual diagnosis or intervention in the EHR. RESULTS In total, 604 pregnancies resulting in birth had records or diagnoses that could be compared against the risk profile; the majority of patients identified as Black (n=482, 79.8%) and aged between 21 and 34 years (n=509, 84.4%). Preeclampsia (n=547, 90.6%) was the most common condition, followed by thromboembolism (n=16, 2.7%) and acute kidney disease or failure (n=13, 2.2%). The average delta was 56.8 (SD 69.7) days between the identification of risk factors by HOPE-CAT and the first date of diagnosis or intervention of a related condition reported in the EHR. HOPE-CAT showed the strongest performance in early risk detection of myocardial infarction at a delta of 65.7 (SD 81.4) days. CONCLUSIONS This study provides additional evidence to support ML in obstetrical patients to enhance the early detection of cardiovascular conditions during pregnancy. ML can synthesize multiday patient presentations to enhance provider decision-making and potentially reduce maternal health disparities.
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Affiliation(s)
- Nawar Shara
- MedStar Health Research Institute, Hyattesville, MD, United States
- Georgetown-Howard Universities Center for Clinical and Translational Science, Washington, DC, DC, United States
| | | | | | - Noor Falah
- MedStar Health Research Institute, Hyattesville, MD, United States
| | - Maryam Ahmad
- MedStar Health Research Institute, Hyattesville, MD, United States
| | | | | | | | - Kelley Anderson
- School of Nursing, Georgetown University, Washington, DC, United States
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Wahbah M, Zitouni MS, Al Sakaji R, Funamoto K, Widatalla N, Krishnan A, Kimura Y, Khandoker AH. A deep learning framework for noninvasive fetal ECG signal extraction. Front Physiol 2024; 15:1329313. [PMID: 38711954 PMCID: PMC11073781 DOI: 10.3389/fphys.2024.1329313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Accepted: 03/22/2024] [Indexed: 05/08/2024] Open
Abstract
Introduction: The availability of proactive techniques for health monitoring is essential to reducing fetal mortality and avoiding complications in fetal wellbeing. In harsh circumstances such as pandemics, earthquakes, and low-resource settings, the incompetence of many healthcare systems worldwide in providing essential services, especially for pregnant women, is critical. Being able to continuously monitor the fetus in hospitals and homes in a direct and fast manner is very important in such conditions. Methods: Monitoring the health of the baby can potentially be accomplished through the computation of vital bio-signal measures using a clear fetal electrocardiogram (ECG) signal. The aim of this study is to develop a framework to detect and identify the R-peaks of the fetal ECG directly from a 12 channel abdominal composite signal. Thus, signals were recorded noninvasively from 70 pregnant (healthy and with health conditions) women with no records of fetal abnormalities. The proposed model employs a recurrent neural network architecture to robustly detect the fetal ECG R-peaks. Results: To test the proposed framework, we performed both subject-dependent (5-fold cross-validation) and independent (leave-one-subject-out) tests. The proposed framework achieved average accuracy values of 94.2% and 88.8%, respectively. More specifically, the leave-one-subject-out test accuracy was 86.7% during the challenging period of vernix caseosa layer formation. Furthermore, we computed the fetal heart rate from the detected R-peaks, and the demonstrated results highlight the robustness of the proposed framework. Discussion: This work has the potential to cater to the critical industry of maternal and fetal healthcare as well as advance related applications.
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Affiliation(s)
- Maisam Wahbah
- College of Engineering and Information Technology, University of Dubai, Dubai, United Arab Emirates
- Department of Biomedical Engineering, Khalifa University, Abu Dhabi, United Arab Emirates
| | - M. Sami Zitouni
- College of Engineering and Information Technology, University of Dubai, Dubai, United Arab Emirates
| | - Raghad Al Sakaji
- Department of Industrial and Systems Engineering, Khalifa University, Abu Dhabi, United Arab Emirates
| | | | - Namareq Widatalla
- Health Engineering Innovation Center (HEIC), Department of Biomedical Engineering, Khalifa University, Abu Dhabi, United Arab Emirates
| | - Anita Krishnan
- Children’s National Hospital, Washington, DC, United States
| | | | - Ahsan H. Khandoker
- Health Engineering Innovation Center (HEIC), Department of Biomedical Engineering, Khalifa University, Abu Dhabi, United Arab Emirates
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Boie S, Glavind J, Bor P, Steer P, Riis AH, Thiesson B, Uldbjerg N. Continued versus discontinued oxytocin stimulation in the active phase of labour (CONDISOX): individual management based on artificial intelligence - a secondary analysis. BMC Pregnancy Childbirth 2024; 24:291. [PMID: 38641779 PMCID: PMC11027395 DOI: 10.1186/s12884-024-06461-8] [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: 12/02/2022] [Accepted: 03/28/2024] [Indexed: 04/21/2024] Open
Abstract
BACKGROUND Current guidelines regarding oxytocin stimulation are not tailored to individuals as they are based on randomised controlled trials. The objective of the study was to develop an artificial intelligence (AI) model for individual prediction of the risk of caesarean delivery (CD) in women with a cervical dilatation of 6 cm after oxytocin stimulation for induced labour. The model included not only variables known when labour induction was initiated but also variables describing the course of the labour induction. METHODS Secondary analysis of data from the CONDISOX randomised controlled trial of discontinued vs. continued oxytocin infusion in the active phase of induced labour. Extreme gradient boosting (XGBoost) software was used to build the prediction model. To explain the impact of the predictors, we calculated Shapley additive explanation (SHAP) values and present a summary SHAP plot. A force plot was used to explain specifics about an individual's predictors that result in a change of the individual's risk output value from the population-based risk. RESULTS Among 1060 included women, 160 (15.1%) were delivered by CD. The XGBoost model found women who delivered vaginally were more likely to be parous, taller, to have a lower estimated birth weight, and to be stimulated with a lower amount of oxytocin. In 108 women (10% of 1060) the model favoured either continuation or discontinuation of oxytocin. For the remaining 90% of the women, the model found that continuation or discontinuation of oxytocin stimulation affected the risk difference of CD by less than 5% points. CONCLUSION In women undergoing labour induction, this AI model based on a secondary analysis of data from the CONDISOX trial may help predict the risk of CD and assist the mother and clinician in individual tailored management of oxytocin stimulation after reaching 6 cm of cervical dilation.
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Affiliation(s)
- Sidsel Boie
- Department of Obstetrics and Gynaecology, Randers Regional Hospital, Randers, Denmark.
| | - Julie Glavind
- Department of Obstetrics and Gynaecology, Aarhus University Hospital, Aarhus, Denmark
| | - Pinar Bor
- Department of Obstetrics and Gynaecology, Aarhus University Hospital, Aarhus, Denmark
| | - Philip Steer
- Academic Department of Obstetrics and Gynaecology, Chelsea and Westminster Hospital, Imperial College London, London, UK
| | | | | | - Niels Uldbjerg
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
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Bernardes LS, Fernandes AM, Carvalho MA, Ottolia J, Hamani M, Oliveira I, Kubota GT, da Silva VA, Veloso A, de Carvalho MHB, de Amorim Filho AG, Arenholt LTS, Leutscher PC, de Andrade DC. Assessment of Human Fetuses Undergoing Acute Pain: Validation of the Fetal-7 Scale. THE JOURNAL OF PAIN 2024:104527. [PMID: 38599264 DOI: 10.1016/j.jpain.2024.104527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 03/24/2024] [Accepted: 04/02/2024] [Indexed: 04/12/2024]
Abstract
Improvements in fetal ultrasound have allowed for the diagnosis and treatment of fetal diseases in the uterus, often through surgery. However, little attention has been drawn to the assessment of fetal pain. To address this gap, a fetal pain scoring system, known as the Fetal-7 scale, was developed. The present study is a full validation of the Fetal-7 scale. The validation involved 2 steps: 1) 4 fetuses with the indication of surgery were evaluated in 3 conditions perioperatively: acute pain, rest, and under loud sound stimulation. Facial expressions were assessed by 30 raters using screenshots from 4D high-definition ultrasound films; 2) assessment of sensitivity and specificity of the Fetal-7 scale in 54 healthy fetuses and 2 fetuses undergoing acute pain after preoperative anesthetic intramuscular injection. There was high internal consistency with Cronbach's alpha (α) of .99. Intrarater reliability of the Fetal-7 scale (test-retest) calculated by intraclass correlation coefficient was .95, and inter-rater reliability was .99. The scale accurately differentiated between healthy fetuses at rest and those experiencing acute pain (sensitivity of 100% and specificity of 94.4%). The Fetal-7 scale is a valid tool for assessing acute pain-related behavior in third-trimester fetuses and may be of value in guiding analgesic procedures efficacy in these patients. Further research is warranted to explore the presence of postoperative pain in fetuses and its effects after birth. PERSPECTIVE: Recordings with 3-dimensional ultrasound of human fetuses undergoing preoperative anesthetic injections revealed complex facial expressions during acute pain, similar to those collected in newborns. This study presented the validation process and cut-off value of the Fetal-7 scale, paving the way for the study of pain before birth in humans.
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Affiliation(s)
- Lisandra S Bernardes
- Center for Clinical Research, North Denmark Regional Hospital, Hjoerring, Denmark; Gynecology and Obstetrics Department, University of Sao Paulo, São Paulo, Brazil; Gynecology and obstetrics, SEPACO Maternity Hospital, São Paulo, Brazil; Department of Gynecology and Obstetrics, North Denmark Regional Hospital, Hjoerring, Denmark.
| | - Ana M Fernandes
- Pain Center, Department of Neurology, University of Sao Paulo, São Paulo, Brazil
| | - Mariana A Carvalho
- Gynecology and Obstetrics Department, University of Sao Paulo, São Paulo, Brazil; Gynecology and obstetrics, SEPACO Maternity Hospital, São Paulo, Brazil
| | - Juliana Ottolia
- Gynecology and Obstetrics Department, University of Sao Paulo, São Paulo, Brazil; Gynecology and obstetrics, SEPACO Maternity Hospital, São Paulo, Brazil
| | - Michele Hamani
- Pain Center, Department of Neurology, University of Sao Paulo, São Paulo, Brazil
| | - Inaeh Oliveira
- Pain Center, Department of Neurology, University of Sao Paulo, São Paulo, Brazil
| | - Gabriel T Kubota
- Pain Center, Department of Neurology, University of Sao Paulo, São Paulo, Brazil
| | - Valquíria A da Silva
- Pain Center, Department of Neurology, University of Sao Paulo, São Paulo, Brazil
| | - Adriano Veloso
- Computational Science Department, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | | | | | - Louise T S Arenholt
- Center for Clinical Research, North Denmark Regional Hospital, Hjoerring, Denmark; Department of Gynecology and Obstetrics, North Denmark Regional Hospital, Hjoerring, Denmark; Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
| | - Peter C Leutscher
- Center for Clinical Research, North Denmark Regional Hospital, Hjoerring, Denmark; Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
| | - Daniel C de Andrade
- Center for Neuroplasticity and Pain, Health Science and Technology, Faculty of Medicine, Aalborg University, Aalborg, Denmark; Pain Center, Department of Neurology, University of Sao Paulo, São Paulo, Brazil
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Peled T, Sela HY, Weiss A, Grisaru-Granovsky S, Agrawal S, Rottenstreich M. Evaluating the validity of ChatGPT responses on common obstetric issues: Potential clinical applications and implications. Int J Gynaecol Obstet 2024. [PMID: 38523565 DOI: 10.1002/ijgo.15501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 02/29/2024] [Accepted: 03/10/2024] [Indexed: 03/26/2024]
Abstract
OBJECTIVE To evaluate the quality of ChatGPT responses to common issues in obstetrics and assess its ability to provide reliable responses to pregnant individuals. The study aimed to examine the responses based on expert opinions using predetermined criteria, including "accuracy," "completeness," and "safety." METHODS We curated 15 common and potentially clinically significant questions that pregnant women are asking. Two native English-speaking women were asked to reframe the questions in their own words, and we employed the ChatGPT language model to generate responses to the questions. To evaluate the accuracy, completeness, and safety of the ChatGPT's generated responses, we developed a questionnaire with a scale of 1 to 5 that obstetrics and gynecology experts from different countries were invited to rate accordingly. The ratings were analyzed to evaluate the average level of agreement and percentage of positive ratings (≥4) for each criterion. RESULTS Of the 42 experts invited, 20 responded to the questionnaire. The combined score for all responses yielded a mean rating of 4, with 75% of responses receiving a positive rating (≥4). While examining specific criteria, the ChatGPT responses were better for the accuracy criterion, with a mean rating of 4.2 and 80% of the questions received a positive rating. The responses scored less for the completeness criterion, with a mean rating of 3.8 and 46.7% of questions received a positive rating. For safety, the mean rating was 3.9 and 53.3% of questions received a positive rating. There was no response with an average negative rating below three. CONCLUSION This study demonstrates promising results regarding potential use of ChatGPT's in providing accurate responses to obstetric clinical questions posed by pregnant women. However, it is crucial to exercise caution when addressing inquiries concerning the safety of the fetus or the mother.
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Affiliation(s)
- Tzuria Peled
- Department of Obstetrics and Gynecology, Shaare Zedek Medical Center, Affiliated with the Hebrew University School of Medicine, Jerusalem, Israel
| | - Hen Y Sela
- Department of Obstetrics and Gynecology, Shaare Zedek Medical Center, Affiliated with the Hebrew University School of Medicine, Jerusalem, Israel
| | - Ari Weiss
- Department of Obstetrics and Gynecology, Shaare Zedek Medical Center, Affiliated with the Hebrew University School of Medicine, Jerusalem, Israel
| | - Sorina Grisaru-Granovsky
- Department of Obstetrics and Gynecology, Shaare Zedek Medical Center, Affiliated with the Hebrew University School of Medicine, Jerusalem, Israel
| | - Swati Agrawal
- Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, Hamilton Health Sciences, McMaster University, Hamilton, Ontario, Canada
| | - Misgav Rottenstreich
- Department of Obstetrics and Gynecology, Shaare Zedek Medical Center, Affiliated with the Hebrew University School of Medicine, Jerusalem, Israel
- Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, Hamilton Health Sciences, McMaster University, Hamilton, Ontario, Canada
- Department of Nursing, Jerusalem College of Technology, Jerusalem, Israel
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Brandão M, Mendes F, Martins M, Cardoso P, Macedo G, Mascarenhas T, Mascarenhas Saraiva M. Revolutionizing Women's Health: A Comprehensive Review of Artificial Intelligence Advancements in Gynecology. J Clin Med 2024; 13:1061. [PMID: 38398374 PMCID: PMC10889757 DOI: 10.3390/jcm13041061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Revised: 02/04/2024] [Accepted: 02/05/2024] [Indexed: 02/25/2024] Open
Abstract
Artificial intelligence has yielded remarkably promising results in several medical fields, namely those with a strong imaging component. Gynecology relies heavily on imaging since it offers useful visual data on the female reproductive system, leading to a deeper understanding of pathophysiological concepts. The applicability of artificial intelligence technologies has not been as noticeable in gynecologic imaging as in other medical fields so far. However, due to growing interest in this area, some studies have been performed with exciting results. From urogynecology to oncology, artificial intelligence algorithms, particularly machine learning and deep learning, have shown huge potential to revolutionize the overall healthcare experience for women's reproductive health. In this review, we aim to establish the current status of AI in gynecology, the upcoming developments in this area, and discuss the challenges facing its clinical implementation, namely the technological and ethical concerns for technology development, implementation, and accountability.
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Affiliation(s)
- Marta Brandão
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (M.B.); (P.C.); (G.M.); (T.M.)
| | - Francisco Mendes
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (F.M.); (M.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| | - Miguel Martins
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (F.M.); (M.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| | - Pedro Cardoso
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (M.B.); (P.C.); (G.M.); (T.M.)
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (F.M.); (M.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| | - Guilherme Macedo
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (M.B.); (P.C.); (G.M.); (T.M.)
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (F.M.); (M.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| | - Teresa Mascarenhas
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (M.B.); (P.C.); (G.M.); (T.M.)
- Department of Obstetrics and Gynecology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Miguel Mascarenhas Saraiva
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (M.B.); (P.C.); (G.M.); (T.M.)
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (F.M.); (M.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
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11
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Deslandes A, Avery J, Chen H, Leonardi M, Condous G, Hull ML. Artificial intelligence as a teaching tool for gynaecological ultrasound: A systematic search and scoping review. Australas J Ultrasound Med 2024; 27:5-11. [PMID: 38434541 PMCID: PMC10902831 DOI: 10.1002/ajum.12368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2024] Open
Abstract
Purpose The aim of this study was to investigate the current application of artificial intelligence (AI) tools in the teaching of ultrasound skills as they pertain to gynaecological ultrasound. Methods A scoping review was performed. Eight databases (MEDLINE, EMBASE, EMCARE, CINAHL, Scopus, Web of Science, IEEE Xplore and ACM digital library) were searched in December 2022 using predefined keywords. All types of publications were eligible for inclusion so long as they reported the use of an AI tool, included reference to or discussion of teaching or the improvement of ultrasound skills and pertained to gynaecological ultrasound. Conference abstracts and non-English language papers which could not be adequately translated into English were excluded. Results The initial database search returned 481 articles. After screening against our inclusion and exclusion criteria, two were deemed to meet the inclusion criteria. Neither of the articles included reported original research (one systematic review and one review article). Neither of the included articles explicitly provided details of specific tools developed for the teaching of ultrasound skills for gynaecological imaging but highlighted similar applications within the field of obstetrics which could potentially be expanded. Conclusion Artificial intelligence can potentially assist in the training of sonographers and other ultrasound operators, including in the field of gynaecological ultrasound. This scoping review revealed however that to date, no original research has been published reporting the use or development of such a tool specifically for gynaecological ultrasound.
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Affiliation(s)
- Alison Deslandes
- Robinson Research InstituteUniversity of AdelaideAdelaideSouth AustraliaAustralia
| | - Jodie Avery
- Robinson Research InstituteUniversity of AdelaideAdelaideSouth AustraliaAustralia
| | - Hsiang‐Ting Chen
- School of Computer and Mathematical SciencesUniversity of AdelaideAdelaideSouth AustraliaAustralia
| | - Mathew Leonardi
- Robinson Research InstituteUniversity of AdelaideAdelaideSouth AustraliaAustralia
- Department of Obstetrics and GynecologyMcMaster UniversityHamiltonOntarioCanada
| | - George Condous
- Robinson Research InstituteUniversity of AdelaideAdelaideSouth AustraliaAustralia
| | - M. Louise Hull
- Robinson Research InstituteUniversity of AdelaideAdelaideSouth AustraliaAustralia
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12
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Gong L, Tang Y, Xie H, Zhang L, Sun Y. Predicting cervical intraepithelial neoplasia and determining the follow-up period in high-risk human papillomavirus patients. Front Oncol 2024; 13:1289030. [PMID: 38298438 PMCID: PMC10827855 DOI: 10.3389/fonc.2023.1289030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 12/18/2023] [Indexed: 02/02/2024] Open
Abstract
Purpose Despite strong efforts to promote human papillomavirus (HPV) vaccine and cervical cancer screening, cervical cancer remains a threat to women's reproductive health. Some high-risk HPV types play a crucial role in the progression of cervical cancer and precancerous lesions. Therefore, HPV screening has become an important means to prevent, diagnose, and triage cervical cancer. This study aims to leverage artificial intelligence to predict individual risks of cervical intraepithelial neoplasia (CIN) in women with high-risk HPV infection and to recommend the appropriate triage strategy and follow-up period according to the risk level. Materials and methods A total of 475 cases were collected in this study. The sources were from the Department of Gynecology and Obstetrics in a tertiary hospital, a case report on HPV from the PubMed website, and clinical data of cervical cancer patients from The Cancer Genome Atlas (TCGA) database. Through in-depth study of the interaction between high-risk HPV and its risk factors, the risk factor relationship diagram structure was constructed. A Classification of Lesion Stages (CLS) algorithm was designed to predict cervical lesion stages. The risk levels of patients were analyzed based on all risk factors, and follow-up periods were formulated for each risk level. Results Our proposed CLS algorithm predicted the probability of occurrence of CIN3-the precancerous lesion stage of cervical cancer. This prediction was based on patients' HPV-16 and -18 infection status, age, presence of persistent infection, and HPV type. Follow-up periods of 3-6 months, 6-12 months, and 3- to 5-year intervals were suggested for high-risk, medium-risk, and low-risk patients, respectively. Conclusion A lesion prediction model was constructed to determine the probabilities of occurrence of CIN by analyzing individual data, such as patient lifestyle, physical assessments, and patient complaints, in order to identify high-risk patients. Furthermore, the potential implications of the calculated features were mined to devise prevention strategies.
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Affiliation(s)
- Ling Gong
- Department of Nursing, School of Nursing, Beihua University, Jilin, China
| | - Yingxuan Tang
- Department of Computer Science and Technology, School of Computer Science, Northeast Electric Power University, Jilin, China
| | - Hua Xie
- Department of Gynecology, Jilin Central General Hospital, Jilin, China
| | - Lu Zhang
- Department of Gynecology, Jilin Central General Hospital, Jilin, China
| | - Yali Sun
- Department of Nursing, School of Nursing, Beihua University, Jilin, China
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13
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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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14
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Jost E, Kosian P, Jimenez Cruz J, Albarqouni S, Gembruch U, Strizek B, Recker F. Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology. J Clin Med 2023; 12:6833. [PMID: 37959298 PMCID: PMC10649694 DOI: 10.3390/jcm12216833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 10/17/2023] [Accepted: 10/25/2023] [Indexed: 11/15/2023] Open
Abstract
Artificial intelligence (AI) has gained prominence in medical imaging, particularly in obstetrics and gynecology (OB/GYN), where ultrasound (US) is the preferred method. It is considered cost effective and easily accessible but is time consuming and hindered by the need for specialized training. To overcome these limitations, AI models have been proposed for automated plane acquisition, anatomical measurements, and pathology detection. This study aims to overview recent literature on AI applications in OB/GYN US imaging, highlighting their benefits and limitations. For the methodology, a systematic literature search was performed in the PubMed and Cochrane Library databases. Matching abstracts were screened based on the PICOS (Participants, Intervention or Exposure, Comparison, Outcome, Study type) scheme. Articles with full text copies were distributed to the sections of OB/GYN and their research topics. As a result, this review includes 189 articles published from 1994 to 2023. Among these, 148 focus on obstetrics and 41 on gynecology. AI-assisted US applications span fetal biometry, echocardiography, or neurosonography, as well as the identification of adnexal and breast masses, and assessment of the endometrium and pelvic floor. To conclude, the applications for AI-assisted US in OB/GYN are abundant, especially in the subspecialty of obstetrics. However, while most studies focus on common application fields such as fetal biometry, this review outlines emerging and still experimental fields to promote further research.
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Affiliation(s)
- Elena Jost
- Department of Obstetrics and Gynecology, University Hospital Bonn, Venusberg Campus 1, 53127 Bonn, Germany
| | - Philipp Kosian
- Department of Obstetrics and Gynecology, University Hospital Bonn, Venusberg Campus 1, 53127 Bonn, Germany
| | - Jorge Jimenez Cruz
- Department of Obstetrics and Gynecology, University Hospital Bonn, Venusberg Campus 1, 53127 Bonn, Germany
| | - Shadi Albarqouni
- Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Venusberg Campus 1, 53127 Bonn, Germany
- Helmholtz AI, Helmholtz Munich, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany
| | - Ulrich Gembruch
- Department of Obstetrics and Gynecology, University Hospital Bonn, Venusberg Campus 1, 53127 Bonn, Germany
| | - Brigitte Strizek
- Department of Obstetrics and Gynecology, University Hospital Bonn, Venusberg Campus 1, 53127 Bonn, Germany
| | - Florian Recker
- Department of Obstetrics and Gynecology, University Hospital Bonn, Venusberg Campus 1, 53127 Bonn, Germany
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15
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Fischer A, Rietveld A, Teunissen P, Hoogendoorn M, Bakker P. What is the future of artificial intelligence in obstetrics? A qualitative study among healthcare professionals. BMJ Open 2023; 13:e076017. [PMID: 37879682 PMCID: PMC10603416 DOI: 10.1136/bmjopen-2023-076017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/27/2023] Open
Abstract
OBJECTIVE This work explores the perceptions of obstetrical clinicians about artificial intelligence (AI) in order to bridge the gap in uptake of AI between research and medical practice. Identifying potential areas where AI can contribute to clinical practice, enables AI research to align with the needs of clinicians and ultimately patients. DESIGN Qualitative interview study. SETTING A national study conducted in the Netherlands between November 2022 and February 2023. PARTICIPANTS Dutch clinicians working in obstetrics with varying relevant work experience, gender and age. ANALYSIS Thematic analysis of qualitative interview transcripts. RESULTS Thirteen gynaecologists were interviewed about hypothetical scenarios of an implemented AI model. Thematic analysis identified two major themes: perceived usefulness and trust. Usefulness involved AI extending human brain capacity in complex pattern recognition and information processing, reducing contextual influence and saving time. Trust required validation, explainability and successful personal experience. This result shows two paradoxes: first, AI is expected to provide added value by surpassing human capabilities, yet also a need to understand the parameters and their influence on predictions for trust and adoption was expressed. Second, participants recognised the value of incorporating numerous parameters into a model, but they also believed that certain contextual factors should only be considered by humans, as it would be undesirable for AI models to use that information. CONCLUSIONS Obstetricians' opinions on the potential value of AI highlight the need for clinician-AI researcher collaboration. Trust can be built through conventional means like randomised controlled trials and guidelines. Holistic impact metrics, such as changes in workflow, not just clinical outcomes, should guide AI model development. Further research is needed for evaluating evolving AI systems beyond traditional validation methods.
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Affiliation(s)
- Anne Fischer
- Department of Obstetrics and Gynecology, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Reproduction and Development Research Institute, Amsterdam, The Netherlands
| | - Anna Rietveld
- Department of Obstetrics and Gynecology, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Amsterdam Reproduction and Development Research Institute, Amsterdam, The Netherlands
| | - Pim Teunissen
- School of Health Professions Education, Faculty of Health Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
- Department of Gynaecology & Obstetrics, Maastricht UMC, Maastricht, The Netherlands
| | - Mark Hoogendoorn
- Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Petra Bakker
- Department of Obstetrics and Gynecology, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Amsterdam Reproduction and Development Research Institute, Amsterdam, The Netherlands
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16
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Suhag A, Kidd J, McGath M, Rajesh R, Gelfinbein J, Cacace N, Monteleone B, Chavez MR. ChatGPT: a pioneering approach to complex prenatal differential diagnosis. Am J Obstet Gynecol MFM 2023; 5:101029. [PMID: 37257586 DOI: 10.1016/j.ajogmf.2023.101029] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Accepted: 05/19/2023] [Indexed: 06/02/2023]
Abstract
This commentary examines how ChatGPT can assist healthcare teams in the prenatal diagnosis of rare and complex cases by creating a differential diagnoses based on deidentified clinical findings, while also acknowledging its limitations.
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Affiliation(s)
- Anju Suhag
- Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, NYU Langone Health, NYU Langone Hospital-Long Island, NYU Long Island School of Medicine, Mineola, NY (Drs Suhag and Kidd, Mses McGath and Cacace, and Dr Chavez).
| | - Jennifer Kidd
- Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, NYU Langone Health, NYU Langone Hospital-Long Island, NYU Long Island School of Medicine, Mineola, NY (Drs Suhag and Kidd, Mses McGath and Cacace, and Dr Chavez)
| | - Meghan McGath
- Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, NYU Langone Health, NYU Langone Hospital-Long Island, NYU Long Island School of Medicine, Mineola, NY (Drs Suhag and Kidd, Mses McGath and Cacace, and Dr Chavez); Department of Clinical Genetics, NYU Langone Hospital-Long Island, Mineola, NY (Mses McGath and Cacace, and Dr Monteleone)
| | - Raeshmma Rajesh
- Department of Obstetrics and Gynecology, Richmond University Medical Center, Staten Island, NY (Dr Rajesh)
| | | | - Nicole Cacace
- Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, NYU Langone Health, NYU Langone Hospital-Long Island, NYU Long Island School of Medicine, Mineola, NY (Drs Suhag and Kidd, Mses McGath and Cacace, and Dr Chavez); Department of Clinical Genetics, NYU Langone Hospital-Long Island, Mineola, NY (Mses McGath and Cacace, and Dr Monteleone)
| | - Berrin Monteleone
- Department of Clinical Genetics, NYU Langone Hospital-Long Island, Mineola, NY (Mses McGath and Cacace, and Dr Monteleone)
| | - Martin R Chavez
- Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, NYU Langone Health, NYU Langone Hospital-Long Island, NYU Long Island School of Medicine, Mineola, NY (Drs Suhag and Kidd, Mses McGath and Cacace, and Dr Chavez)
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17
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Chavez MR, Butler TS, Rekawek P, Heo H, Kinzler WL. Chat Generative Pre-trained Transformer: why we should embrace this technology. Am J Obstet Gynecol 2023; 228:706-711. [PMID: 36924908 DOI: 10.1016/j.ajog.2023.03.010] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 03/06/2023] [Accepted: 03/06/2023] [Indexed: 03/17/2023]
Abstract
With the advent of artificial intelligence that not only can learn from us but also can communicate with us in plain language, humans are embarking on a brave new future. The interaction between humans and artificial intelligence has never been so widespread. Chat Generative Pre-trained Transformer is an artificial intelligence resource that has potential uses in the practice of medicine. As clinicians, we have the opportunity to help guide and develop new ways to use this powerful tool. Optimal use of any tool requires a certain level of comfort. This is best achieved by appreciating its power and limitations. Being part of the process is crucial in maximizing its use in our field. This clinical opinion demonstrates the potential uses of Chat Generative Pre-trained Transformer for obstetrician-gynecologists and encourages readers to serve as the driving force behind this resource.
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Affiliation(s)
- Martin R Chavez
- Division of Maternal-Fetal Medicine, Department of Obstetrics Gynecology, New York University Langone Hospital-Long Island, New York University Long Island School of Medicine, Mineola, NY.
| | - Thomas S Butler
- New York University Langone Reproductive Specialists of New York, New York University Langone Hospital-Long Island, New York University Langone Long Island School of Medicine, Mineola, New York
| | - Patricia Rekawek
- Division of Maternal-Fetal Medicine, Department of Obstetrics Gynecology, New York University Langone Hospital-Long Island, New York University Long Island School of Medicine, Mineola, NY
| | - Hye Heo
- Division of Maternal-Fetal Medicine, Department of Obstetrics Gynecology, New York University Langone Hospital-Long Island, New York University Long Island School of Medicine, Mineola, NY
| | - Wendy L Kinzler
- Division of Maternal-Fetal Medicine, Department of Obstetrics Gynecology, New York University Langone Hospital-Long Island, New York University Long Island School of Medicine, Mineola, NY
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18
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Arain Z, Iliodromiti S, Slabaugh G, David AL, Chowdhury TT. Machine learning and disease prediction in obstetrics. Curr Res Physiol 2023; 6:100099. [PMID: 37324652 PMCID: PMC10265477 DOI: 10.1016/j.crphys.2023.100099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 05/09/2023] [Indexed: 06/17/2023] Open
Abstract
Machine learning technologies and translation of artificial intelligence tools to enhance the patient experience are changing obstetric and maternity care. An increasing number of predictive tools have been developed with data sourced from electronic health records, diagnostic imaging and digital devices. In this review, we explore the latest tools of machine learning, the algorithms to establish prediction models and the challenges to assess fetal well-being, predict and diagnose obstetric diseases such as gestational diabetes, pre-eclampsia, preterm birth and fetal growth restriction. We discuss the rapid growth of machine learning approaches and intelligent tools for automated diagnostic imaging of fetal anomalies and to asses fetoplacental and cervix function using ultrasound and magnetic resonance imaging. In prenatal diagnosis, we discuss intelligent tools for magnetic resonance imaging sequencing of the fetus, placenta and cervix to reduce the risk of preterm birth. Finally, the use of machine learning to improve safety standards in intrapartum care and early detection of complications will be discussed. The demand for technologies to enhance diagnosis and treatment in obstetrics and maternity should improve frameworks for patient safety and enhance clinical practice.
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Affiliation(s)
- Zara Arain
- Centre for Bioengineering, School of Engineering and Materials Science, Queen Mary University of London, Mile End Road, London, E1 4NS, UK
| | - Stamatina Iliodromiti
- Women's Health Research Unit, Wolfson Institute of Population Health, Queen Mary University of London, 58 Turner Street, London, E1 2AB, UK
| | - Gregory Slabaugh
- Digital Environment Research Institute, School of Electronic Engineering and Computer Science, Queen Mary University of London, London, E1 1HH, UK
| | - Anna L. David
- Elizabeth Garrett Anderson Institute for Women's Health, University College London, Medical School Building, Huntley Street, London, WC1E 6AU, UK
| | - Tina T. Chowdhury
- Centre for Bioengineering, School of Engineering and Materials Science, Queen Mary University of London, Mile End Road, London, E1 4NS, UK
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19
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Du Y, McNestry C, Wei L, Antoniadi AM, McAuliffe FM, Mooney C. Machine learning-based clinical decision support systems for pregnancy care: A systematic review. Int J Med Inform 2023; 173:105040. [PMID: 36907027 DOI: 10.1016/j.ijmedinf.2023.105040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 01/12/2023] [Accepted: 03/03/2023] [Indexed: 03/11/2023]
Abstract
BACKGROUND Clinical decision support systems (CDSSs) can provide various functions and advantages to healthcare delivery. Quality healthcare during pregnancy and childbirth is of vital importance, and machine learning-based CDSSs have shown positive impact on pregnancy care. OBJECTIVE This paper aims to investigate what has been done in CDSSs in the context of pregnancy care using machine learning, and what aspects require attention from future researchers. METHODS We conducted a systematic review of existing literature following a structured process of literature search, paper selection and filtering, and data extraction and synthesis. RESULTS 17 research papers were identified on the topic of CDSS development for different aspects of pregnancy care using various machine learning algorithms. We discovered an overall lack of explainability in the proposed models. We also observed a lack of experimentation, external validation and discussion around culture, ethnicity and race from the source data, with most studies using data from a single centre or country, and an overall lack of awareness of applicability and generalisability of the CDSSs regarding different populations. Finally, we found a gap between machine learning practices and CDSS implementation, and an overall lack of user testing. CONCLUSION Machine learning-based CDSSs are still under-explored in the context of pregnancy care. Despite the open problems that remain, the few studies that tested a CDSS for pregnancy care reported positive effects, reinforcing the potential of such systems to improve clinical practice. We encourage future researchers to take into consideration the aspects we identified in order for their work to translate into clinical use.
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Affiliation(s)
- Yuhan Du
- UCD School of Computer Science, University College Dublin, Dublin, Ireland
| | - Catherine McNestry
- UCD Perinatal Research Centre, School of Medicine, University College Dublin, National Maternity Hospital, Dublin, Ireland
| | - Lan Wei
- UCD School of Computer Science, University College Dublin, Dublin, Ireland
| | | | - Fionnuala M McAuliffe
- UCD Perinatal Research Centre, School of Medicine, University College Dublin, National Maternity Hospital, Dublin, Ireland
| | - Catherine Mooney
- UCD School of Computer Science, University College Dublin, Dublin, Ireland.
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20
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Papastefanou I, Nicolaides KH, Salomon LJ. Audit of fetal biometry: understanding sources of error to improve our practice. ULTRASOUND IN OBSTETRICS & GYNECOLOGY : THE OFFICIAL JOURNAL OF THE INTERNATIONAL SOCIETY OF ULTRASOUND IN OBSTETRICS AND GYNECOLOGY 2023; 61:431-435. [PMID: 36647209 DOI: 10.1002/uog.26156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 12/15/2022] [Accepted: 12/20/2022] [Indexed: 06/17/2023]
Affiliation(s)
- I Papastefanou
- Fetal Medicine Research Institute, King's College Hospital, London, UK
- Department of Women and Children's Health, Faculty of Life Sciences & Medicine, King's College London, London, UK
| | - K H Nicolaides
- Fetal Medicine Research Institute, King's College Hospital, London, UK
| | - L J Salomon
- Department of Obstetrics, Fetal Medicine and Surgery, Necker-Enfants Malades Hospital, AP-HP, Paris, France
- URP FETUS 7328 and LUMIERE Platform, University of Paris Cité, Institut Imagine, Paris, France
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21
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Sarno L, Neola D, Carbone L, Saccone G, Carlea A, Miceli M, Iorio GG, Mappa I, Rizzo G, Girolamo RD, D'Antonio F, Guida M, Maruotti GM. Use of artificial intelligence in obstetrics: not quite ready for prime time. Am J Obstet Gynecol MFM 2023; 5:100792. [PMID: 36356939 DOI: 10.1016/j.ajogmf.2022.100792] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 10/18/2022] [Accepted: 10/28/2022] [Indexed: 11/09/2022]
Abstract
Artificial intelligence is finding several applications in healthcare settings. This study aimed to report evidence on the effectiveness of artificial intelligence application in obstetrics. Through a narrative review of literature, we described artificial intelligence use in different obstetrical areas as follows: prenatal diagnosis, fetal heart monitoring, prediction and management of pregnancy-related complications (preeclampsia, preterm birth, gestational diabetes mellitus, and placenta accreta spectrum), and labor. Artificial intelligence seems to be a promising tool to help clinicians in daily clinical activity. The main advantages that emerged from this review are related to the reduction of inter- and intraoperator variability, time reduction of procedures, and improvement of overall diagnostic performance. However, nowadays, the diffusion of these systems in routine clinical practice raises several issues. Reported evidence is still very limited, and further studies are needed to confirm the clinical applicability of artificial intelligence. Moreover, better training of clinicians designed to use these systems should be ensured, and evidence-based guidelines regarding this topic should be produced to enhance the strengths of artificial systems and minimize their limits.
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Affiliation(s)
- Laura Sarno
- Gynecology and Obstetrics Unit, Department of Neuroscience, Reproductive Sciences and Dentistry, School of Medicine, University of Naples Federico II, Naples, Italy (Dr Sarno, Dr Neola, Dr Carbone, Dr Saccone, Dr Carlea, Dr Miceli, Dr Iorio, Dr Girolamo, and Dr Guida)
| | - Daniele Neola
- Gynecology and Obstetrics Unit, Department of Neuroscience, Reproductive Sciences and Dentistry, School of Medicine, University of Naples Federico II, Naples, Italy (Dr Sarno, Dr Neola, Dr Carbone, Dr Saccone, Dr Carlea, Dr Miceli, Dr Iorio, Dr Girolamo, and Dr Guida).
| | - Luigi Carbone
- Gynecology and Obstetrics Unit, Department of Neuroscience, Reproductive Sciences and Dentistry, School of Medicine, University of Naples Federico II, Naples, Italy (Dr Sarno, Dr Neola, Dr Carbone, Dr Saccone, Dr Carlea, Dr Miceli, Dr Iorio, Dr Girolamo, and Dr Guida)
| | - Gabriele Saccone
- Gynecology and Obstetrics Unit, Department of Neuroscience, Reproductive Sciences and Dentistry, School of Medicine, University of Naples Federico II, Naples, Italy (Dr Sarno, Dr Neola, Dr Carbone, Dr Saccone, Dr Carlea, Dr Miceli, Dr Iorio, Dr Girolamo, and Dr Guida)
| | - Annunziata Carlea
- Gynecology and Obstetrics Unit, Department of Neuroscience, Reproductive Sciences and Dentistry, School of Medicine, University of Naples Federico II, Naples, Italy (Dr Sarno, Dr Neola, Dr Carbone, Dr Saccone, Dr Carlea, Dr Miceli, Dr Iorio, Dr Girolamo, and Dr Guida)
| | - Marco Miceli
- Gynecology and Obstetrics Unit, Department of Neuroscience, Reproductive Sciences and Dentistry, School of Medicine, University of Naples Federico II, Naples, Italy (Dr Sarno, Dr Neola, Dr Carbone, Dr Saccone, Dr Carlea, Dr Miceli, Dr Iorio, Dr Girolamo, and Dr Guida); CEINGE Biotecnologie Avanzate, Naples, Italy (Dr Miceli)
| | - Giuseppe Gabriele Iorio
- Gynecology and Obstetrics Unit, Department of Neuroscience, Reproductive Sciences and Dentistry, School of Medicine, University of Naples Federico II, Naples, Italy (Dr Sarno, Dr Neola, Dr Carbone, Dr Saccone, Dr Carlea, Dr Miceli, Dr Iorio, Dr Girolamo, and Dr Guida)
| | - Ilenia Mappa
- Division of Maternal Fetal Medicine, Department of Obstetrics and Gynecology, University of Rome Tor Vergata, Rome, Italy (Dr Mappa and Dr Rizzo)
| | - Giuseppe Rizzo
- Division of Maternal Fetal Medicine, Department of Obstetrics and Gynecology, University of Rome Tor Vergata, Rome, Italy (Dr Mappa and Dr Rizzo)
| | - Raffaella Di Girolamo
- Gynecology and Obstetrics Unit, Department of Neuroscience, Reproductive Sciences and Dentistry, School of Medicine, University of Naples Federico II, Naples, Italy (Dr Sarno, Dr Neola, Dr Carbone, Dr Saccone, Dr Carlea, Dr Miceli, Dr Iorio, Dr Girolamo, and Dr Guida)
| | - Francesco D'Antonio
- Center for Fetal Care and High Risk Pregnancy, Department of Obstetrics and Gynecology, University G. D'Annunzio of Chieti-Pescara, Chieti, Italy (Dr D'Antonio)
| | - Maurizio Guida
- Gynecology and Obstetrics Unit, Department of Neuroscience, Reproductive Sciences and Dentistry, School of Medicine, University of Naples Federico II, Naples, Italy (Dr Sarno, Dr Neola, Dr Carbone, Dr Saccone, Dr Carlea, Dr Miceli, Dr Iorio, Dr Girolamo, and Dr Guida)
| | - Giuseppe Maria Maruotti
- Gynecology and Obstetrics Unit, Department of Public Health, School of Medicine, University of Naples Federico II, Naples, Italy (Dr Maruotti)
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