Artificial Intelligence: A New Paradigm in Obstetrics and Gynecology Research and Clinical Practice

Machine Learning for Predicting Stillbirth: A Systematic Review

Stillbirth is a major global issue, with over 5 million cases each year. The multifactorial nature of stillbirth makes it difficult to predict. Artificial intelligence (AI) and machine learning (ML) have the potential to enhance clinical decision-making and enable precise assessments. This study reviewed the literature on predictive ML models for stillbirth highlighting input characteristics, performance metrics, and validation. The PubMed, Cochrane, and Web of Science databases were searched for studies using AI to develop predictive models for stillbirth. Findings were analyzed qualitatively using narrative synthesis and graphics. Risk of bias and the applicability of the studies were assessed using PROBAST. Model design and performance were discussed. Eight studies involving 14,840,654 women with gestational ages ranging from 20 weeks to full term were included in the qualitative analysis. Most studies used neural networks, random forests, and logistic regression algorithms. The number of predictive features varied from 14 to 53. Only 50% of studies validated the models. Cross-validation was commonly employed, and only 25% of studies performed external validation. All studies reported area under the curve as a performance metric (range 0.54-0.9), and five studies reported sensitivity (range, 60- 90%) and specificity (range, 64 - 93.3%). A stacked ensemble model that analyzed 53 features performed better than other models (AUC = 0.9; sensitivity and specificity > 85%). Available ML models can attain a considerable degree of accuracy for prediction of stillbirth; however, these models require further development before they can be applied in a clinical setting.

Exploring the potential of machine learning in gynecological care: a review

Gynecological health remains a critical aspect of women's overall well-being, with profound implications for maternal and reproductive outcomes. This comprehensive review synthesizes the current state of knowledge on four pivotal aspects of gynecological health: preterm birth, breast cancer and cervical cancer and infertility treatment. Machine learning (ML) has emerged as a transformative technology with the potential to revolutionize gynecology and women's healthcare. The subsets of AI, namely, machine learning (ML) and deep learning (DL) methods, have aided in detecting complex patterns from huge datasets and using such patterns in making predictions. This paper investigates how machine learning (ML) algorithms are employed in the field of gynecology to tackle crucial issues pertaining to women's health. This paper also investigates the integration of ultrasound technology with artificial intelligence (AI) during the initial, intermediate, and final stages of pregnancy. Additionally, it delves into the diverse applications of AI throughout each trimester.This review paper provides an overview of machine learning (ML) models, introduces natural language processing (NLP) concepts, including ChatGPT, and discusses the clinical applications of artificial intelligence (AI) in gynecology. Additionally, the paper outlines the challenges in utilizing machine learning within the field of gynecology.

Use of artificial intelligence in obstetric and gynaecological diagnostics: a protocol for a systematic review and meta-analysis

INTRODUCTION

Emerging developments in applications of artificial intelligence (AI) in healthcare offer the opportunity to improve diagnostic capabilities in obstetrics and gynaecology (O&G), ensuring early detection of pathology, optimal management and improving survival. Consensus on a robust AI healthcare framework is crucial for standardising protocols that promote data privacy and transparency, minimise bias, and ensure patient safety. Here, we describe the study protocol for a systematic review and meta-analysis to evaluate current applications of AI in O&G diagnostics with consideration of reporting standards used and their ethical implications. This protocol is written following the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) 2015 checklist.

METHODS AND ANALYSIS

The study objective is to explore the current application of AI in O&G diagnostics and assess the reporting standards used in these studies. Electronic bibliographic databases MEDLINE, EMBASE and Cochrane will be searched. Study selection, data extraction and subsequent narrative synthesis and meta-analyses will be carried out following the PRISMA-P guidelines. Included papers will be English-language full-text articles from May 2015 to March 2024, which provide original data, as AI has been redefined in recent literature. Papers must use AI as the predictive method, focusing on improving O&G diagnostic outcomes.We will evaluate the reporting standards including the risk of bias, lack of transparency and consider the ethical implications and potential harm to patients. Outcome measures will involve assessing the included studies against gold-standard criteria for robustness of model development (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis, model predictive performance, model risk of bias and applicability (Prediction model Risk Of Bias Assessment Tool and study reporting (Consolidated Standards of Reporting Trials-AI) guidance.

ETHICS AND DISSEMINATION

Ethical approval is not required for this systematic review. Findings will be shared through peer-reviewed publications. There will be no patient or public involvement in this study.

PROSPERO REGISTRATION NUMBER

CRD42022357024 .

Medical students' perceptions of an artificial intelligence (AI) assisted diagnosing program

As artificial intelligence (AI) assisted diagnosing systems become accessible and user-friendly, evaluating how first-year medical students perceive such systems holds substantial importance in medical education. This study aimed to assess medical students' perceptions of an AI-assisted diagnostic tool known as 'Glass AI.' Data was collected from first year medical students enrolled in a 1.5-week Cell Physiology pre-clerkship unit. Students voluntarily participated in an activity that involved implementation of Glass AI to solve a clinical case. A questionnaire was designed using 3 domains: 1) immediate experience with Glass AI, 2) potential for Glass AI utilization in medical education, and 3) student deliberations of AI-assisted diagnostic systems for future healthcare environments. 73/202 (36.10%) of students completed the survey. 96% of the participants noted that Glass AI increased confidence in the diagnosis, 43% thought Glass AI lacked sufficient explanation, and 68% expressed risk concerns for the physician workforce. Students expressed future positive outlooks involving AI-assisted diagnosing systems in healthcare, provided strict regulations, are set to protect patient privacy and safety, address legal liability, remove system biases, and improve quality of patient care. In conclusion, first year medical students are aware that AI will play a role in their careers as students and future physicians.

Establishment of a model for predicting preterm birth based on the machine learning algorithm

BACKGROUND

The purpose of this study was to construct a preterm birth prediction model based on electronic health records and to provide a reference for preterm birth prediction in the future.

METHODS

This was a cross-sectional design. The risk factors for the outcomes of preterm birth were assessed by multifactor logistic regression analysis. In this study, a logical regression model, decision tree, Naive Bayes, support vector machine, and AdaBoost are used to construct the prediction model. Accuracy, recall, precision, F1 value, and receiver operating characteristic curve, were used to evaluate the prediction performance of the model, and the clinical application of the model was verified.

RESULTS

A total of 5411 participants were included and were used for model construction. AdaBoost model has the best prediction ability among the five models. The accuracy of the model for the prediction of "non-preterm birth" was the highest, reaching 100%, and that of "preterm birth" was 72.73%.

CONCLUSIONS

By constructing a preterm birth prediction model based on electronic health records, we believe that machine algorithms have great potential for preterm birth identification. However, more relevant studies are needed before its application in the clinic.

Artificial Intelligence in Gynaecological Malignancies: Perspectives of a Clinical Oncologist

Gynecological malignancies are treated with a multi-disciplinary approach. There are two important factors, the stage and Karnofsky performance status scale (KPS) of patients, which guide the treatment strategy by single or multiple modalities in terms of surgery, radiotherapy, or chemotherapy. Various aspects are included in the workflow of gynecological malignancies, like screening, diagnosis in each individual, treatment modalities, and finally, follow-up to see for outcomes leading to the development of new research protocols. The quality data plays an important role in every step. Artificial Intelligence (AI) will play an important role if it is developed in the above-mentioned steps. AI is already established partially in every aspect of the management of gynecological cancer. It needs to be strengthened and incorporated further in a more robust form. This needs an association between clinicians, software engineers, and stakeholders. This article reviews the role of AI in various steps of the workflow of gynecological malignancies and discusses a few clinical aspects that may be researched to find solutions by AI.

The role of artificial intelligence in the future of urogynecology

Artificial intelligence (AI) in medicine is a rapidly growing field aimed at using machine learning models to improve health outcomes and patient experiences. Many new platforms have become accessible and therefore it seems inevitable that we consider how to implement them in our day-to-day practice. Currently, the specialty of urogynecology faces new challenges as the population grows, life expectancy increases, and quality of life expectation is much improved. As AI has a lot of potential to promote the discipline of urogynecology, we aim to explore its abilities and possible use in the future. Challenges and risks are associated with using AI, and a responsible use of such resources is required.

A Comprehensive Review of the Role of Artificial Intelligence in Obstetrics and Gynecology

The exponential growth of artificial intelligence (AI) has fascinated its application in various fields and so in the field of healthcare. Technological advancements in theories and learning algorithms and the availability of processing through huge datasets have created a breakthrough in the medical field with computing systems. AI can potentially drive clinicians and practitioners with appropriate decisions in managing cases and reaching a diagnosis, so its application is extensively spread in the medical field. Thus, computerized algorithms have made predictions so simple and accurate. This is because AI can proffer information accurately even to many patients. Furthermore, the subsets of AI, namely, machine learning (ML) and deep learning (DL) methods, have aided in detecting complex patterns from huge datasets and using such patterns in making predictions. Despite numerous challenges, AI implementation in obstetrics and gynecology is found to have a spellbound development. Therefore, this review propounds exploring the implementation of AI in obstetrics and gynecology to improve the outcomes and clinical experience. In that context, the evolution and progress of AI, the role of AI in ultrasound diagnosis in distinct phases of pregnancy, clinical benefits, preterm birth postpartum period, and applications of AI in gynecology are elucidated in this review with future recommendations.

Artificial Intelligence for Predicting and Diagnosing Complications of Diabetes

Artificial intelligence can use real-world data to create models capable of making predictions and medical diagnosis for diabetes and its complications. The aim of this commentary article is to provide a general perspective and present recent advances on how artificial intelligence can be applied to improve the prediction and diagnosis of six significant complications of diabetes including (1) gestational diabetes, (2) hypoglycemia in the hospital, (3) diabetic retinopathy, (4) diabetic foot ulcers, (5) diabetic peripheral neuropathy, and (6) diabetic nephropathy.

Towards deep observation: A systematic survey on artificial intelligence techniques to monitor fetus via Ultrasound Images

Several reviews have been conducted regarding artificial intelligence (AI) techniques to improve pregnancy outcomes. But they are not focusing on ultrasound images. This survey aims to explore how AI can assist with fetal growth monitoring via ultrasound image. We reported our findings using the guidelines for PRISMA. We conducted a comprehensive search of eight bibliographic databases. Out of 1269 studies 107 are included. We found that 2D ultrasound images were more popular (88) than 3D and 4D ultrasound images (19). Classification is the most used method (42), followed by segmentation (31), classification integrated with segmentation (16) and other miscellaneous methods such as object-detection, regression, and reinforcement learning (18). The most common areas that gained traction within the pregnancy domain were the fetus head (43), fetus body (31), fetus heart (13), fetus abdomen (10), and the fetus face (10). This survey will promote the development of improved AI models for fetal clinical applications.

•

Artificial intelligence studies to monitor fetal development via ultrasound images

•

Fetal issues categorized based on four categories — general, head, heart, face, abdomen

•

The most used AI techniques are classification, segmentation, object detection, and RL

•

The research and practical implications are included.

Modification of Case-Based Reasoning Similarity Formula to Enhance the Performance of Smart System in Handling the Complaints of in vitro Fertilization Program Patients

Objectives

Eighty percent of in vitro fertilization (IVF) patients have high anxiety levels, which influence the success of IVF and drive IVF patients to quickly report any abnormal symptoms. Rapid responses from fertility subspecialist doctors may reduce patients’ anxiety levels, but fertility subspecialist doctors’ high workload and their patients’ worsening health conditions make them unable to handle IVF patients’ complaints quickly. Research suggests that smart systems using case-based reasoning (CBR) can help doctors handle patients quickly. However, a prior study reported enhanced accuracy by modifying the CBR similarity formula based on Lin’s similarity theory to generate the Chris case-based reasoning (CCBR) similarity formula.

Methods

The data were validated through interviews with two fertility subspecialist doctors, interviews with two IVF patients, a questionnaire administered to 17 community members, the relevant literature, and 256 records with data on IVF patients’ complaints and how they were handled. An experiment compared the performance of the CBR similarity formula algorithm with the CCBR similarity formula algorithm.

Results

A confusion matrix showed that the CCBR similarity formula had an accuracy value of 52.58% and a precision value of 100%. Fertility subspecialist doctors stated that 89.69% of the CCBR similarity formula recommendations were accurate.

Conclusions

We recommend applying a combination of the CCBR similarity formula and a minimum reference value of 80% with a CBR smart system for handling IVF patients’ complaints. This recommendation for an accurate system produced by the CBR similarity formula may help fertility subspecialist doctors handle IVF patients’ complaints.

Using Machine Learning to Predict Complications in Pregnancy: A Systematic Review

Introduction: Artificial intelligence is widely used in medical field, and machine learning has been increasingly used in health care, prediction, and diagnosis and as a method of determining priority. Machine learning methods have been features of several tools in the fields of obstetrics and childcare. This present review aims to summarize the machine learning techniques to predict perinatal complications.

Objective: To identify the applicability and performance of machine learning methods used to identify pregnancy complications.

Methods: A total of 98 articles were obtained with the keywords “machine learning,” “deep learning,” “artificial intelligence,” and accordingly as they related to perinatal complications (“complications in pregnancy,” “pregnancy complications”) from three scientific databases: PubMed, Scopus, and Web of Science. These were managed on the Mendeley platform and classified using the PRISMA method.

Results: A total of 31 articles were selected after elimination according to inclusion and exclusion criteria. The features used to predict perinatal complications were primarily electronic medical records (48%), medical images (29%), and biological markers (19%), while 4% were based on other types of features, such as sensors and fetal heart rate. The main perinatal complications considered in the application of machine learning thus far are pre-eclampsia and prematurity. In the 31 studies, a total of sixteen complications were predicted. The main precision metric used is the AUC. The machine learning methods with the best results were the prediction of prematurity from medical images using the support vector machine technique, with an accuracy of 95.7%, and the prediction of neonatal mortality with the XGBoost technique, with 99.7% accuracy.

Conclusion: It is important to continue promoting this area of research and promote solutions with multicenter clinical applicability through machine learning to reduce perinatal complications. This systematic review contributes significantly to the specialized literature on artificial intelligence and women’s health.

Machine Learning Algorithm to Predict Acidemia Using Electronic Fetal Monitoring Recording Parameters

Background: Electronic fetal monitoring (EFM) is the universal method for the surveillance of fetal well-being in intrapartum. Our objective was to predict acidemia from fetal heart signal features using machine learning algorithms. Methods: A case–control 1:2 study was carried out compromising 378 infants, born in the Miguel Servet University Hospital, Spain. Neonatal acidemia was defined as pH < 7.10. Using EFM recording logistic regression, random forest and neural networks models were built to predict acidemia. Validation of models was performed by means of discrimination, calibration, and clinical utility. Results: Best performance was attained using a random forest model built with 100 trees. The discrimination ability was good, with an area under the Receiver Operating Characteristic curve (AUC) of 0.865. The calibration showed a slight overestimation of acidemia occurrence for probabilities above 0.4. The clinical utility showed that for 33% cutoff point, missing 5% of acidotic cases, 46% of unnecessary cesarean sections could be prevented. Logistic regression and neural networks showed similar discrimination ability but with worse calibration and clinical utility. Conclusions: The combination of the variables extracted from EFM recording provided a predictive model of acidemia that showed good accuracy and provides a practical tool to prevent unnecessary cesarean sections.

Artificial intelligence, machine learning, and deep learning for clinical outcome prediction

AI is a broad concept, grouping initiatives that use a computer to perform tasks that would usually require a human to complete. AI methods are well suited to predict clinical outcomes. In practice, AI methods can be thought of as functions that learn the outcomes accompanying standardized input data to produce accurate outcome predictions when trialed with new data. Current methods for cleaning, creating, accessing, extracting, augmenting, and representing data for training AI clinical prediction models are well defined. The use of AI to predict clinical outcomes is a dynamic and rapidly evolving arena, with new methods and applications emerging. Extraction or accession of electronic health care records and combining these with patient genetic data is an area of present attention, with tremendous potential for future growth. Machine learning approaches, including decision tree methods of Random Forest and XGBoost, and deep learning techniques including deep multi-layer and recurrent neural networks, afford unique capabilities to accurately create predictions from high dimensional, multimodal data. Furthermore, AI methods are increasing our ability to accurately predict clinical outcomes that previously were difficult to model, including time-dependent and multi-class outcomes. Barriers to robust AI-based clinical outcome model deployment include changing AI product development interfaces, the specificity of regulation requirements, and limitations in ensuring model interpretability, generalizability, and adaptability over time.

Validating machine learning models for the prediction of labour induction intervention using routine data: a registry-based retrospective cohort study at a tertiary hospital in northern Tanzania

OBJECTIVES

We aimed at identifying the important variables for labour induction intervention and assessing the predictive performance of machine learning algorithms.

SETTING

We analysed the birth registry data from a referral hospital in northern Tanzania. Since July 2000, every birth at this facility has been recorded in a specific database.

PARTICIPANTS

21 578 deliveries between 2000 and 2015 were included. Deliveries that lacked information regarding the labour induction status were excluded.

PRIMARY OUTCOME

Deliveries involving labour induction intervention.

RESULTS

Parity, maternal age, body mass index, gestational age and birth weight were all found to be important predictors of labour induction. Boosting method demonstrated the best discriminative performance (area under curve, AUC=0.75: 95% CI (0.73 to 0.76)) while logistic regression presented the least (AUC=0.71: 95% CI (0.70 to 0.73)). Random forest and boosting algorithms showed the highest net-benefits as per the decision curve analysis.

CONCLUSION

All of the machine learning algorithms performed well in predicting the likelihood of labour induction intervention. Further optimisation of these classifiers through hyperparameter tuning may result in an improved performance. Extensive research into the performance of other classifier algorithms is warranted.

Clinical Effects of Form-Based Management of Forceps Delivery under Intelligent Medical Model

Background

Forceps delivery is one of the most important measures to facilitate vaginal delivery. It can reduce the rate of first cesarean delivery. Frustratingly, adverse maternal and neonatal outcomes associated with forceps delivery have been frequently reported in recent years. There are two major reasons: one is that the abilities of doctors and midwives in forceps delivery vary from hospital to hospital and the other one is lack of regulations in the management of forceps delivery. In order to improve the success rate of forceps delivery and reduce the incidence of maternal and neonatal complications, we applied form-based management to forceps delivery under an intelligent medical model. The aim of this work is to explore the clinical effects of form-based management of forceps delivery.

Methods

Patients with forceps delivery in Maternal and Child Health Hospital Affiliated to Nanchang University were divided into two groups: form-based patients from January 1, 2019, to December 31, 2020, were selected as the study group, while traditional protocol patients from January 1, 2017, to December 31, 2018, were chosen as the control group. Then, we compared the maternal and neonatal outcomes of these two groups.

Results

There were significant differences in the maternal and neonatal adverse outcomes such as rate of postpartum hemorrhage, degree of perineal laceration, and incidence of neonatal facial skin abrasions between the two groups, whereas differences in the incidence of asphyxia and intracranial hemorrhage were not significant.

Conclusions

Form-based management could help us assess the security of forceps delivery comprehensively, as it could not only improve the success rate of the one-time forceps traction scheme but also reduce the incidence of maternal and neonatal adverse outcomes effectively.

A, Adnan AS, Alqadhi AM, Abdullah CK, A.K. AS, Olaiya NG, Abdat M. The Role of Biopolymer-Based Materials in Obstetrics and Gynecology Applications: A Review

Biopolymers have gained tremendous attention in many daily life applications, including medical applications, in the past few years. Obstetrics and gynecology are two fields dealing with sensitive parts of the woman's body and her newborn baby, which are normally associated with many issues such as toxicity, infections, and even gene alterations. Medical professions that use screening, examination, pre, and post-operation materials should benefit from a better understanding of each type of material's characteristics, health, and even environmental effects. The underlying principles of biopolymer-based materials for different obstetric and gynecologic applications may discover various advantages and benefits of using such materials. This review presents the health impact of conventional polymer-based materials on pregnant women's health and highlights the potential use of biopolymers as a safer option. The recent works on utilizing different biopolymer-based materials in obstetric and gynecologic are presented in this review, which includes suture materials in obstetric and gynecologic surgeries, cosmetic and personal care products, vaginal health, and drug delivery; as well as a wound dressing and healing materials. This review highlights the main issues and challenges of biopolymers in obstetric and gynecologic applications.

Comparison of Multivariable Logistic Regression and Other Machine Learning Algorithms for Prognostic Prediction Studies in Pregnancy Care: Systematic Review and Meta-Analysis

BACKGROUND

Predictions in pregnancy care are complex because of interactions among multiple factors. Hence, pregnancy outcomes are not easily predicted by a single predictor using only one algorithm or modeling method.

OBJECTIVE

This study aims to review and compare the predictive performances between logistic regression (LR) and other machine learning algorithms for developing or validating a multivariable prognostic prediction model for pregnancy care to inform clinicians' decision making.

METHODS

Research articles from MEDLINE, Scopus, Web of Science, and Google Scholar were reviewed following several guidelines for a prognostic prediction study, including a risk of bias (ROB) assessment. We report the results based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Studies were primarily framed as PICOTS (population, index, comparator, outcomes, timing, and setting): Population: men or women in procreative management, pregnant women, and fetuses or newborns; Index: multivariable prognostic prediction models using non-LR algorithms for risk classification to inform clinicians' decision making; Comparator: the models applying an LR; Outcomes: pregnancy-related outcomes of procreation or pregnancy outcomes for pregnant women and fetuses or newborns; Timing: pre-, inter-, and peripregnancy periods (predictors), at the pregnancy, delivery, and either puerperal or neonatal period (outcome), and either short- or long-term prognoses (time interval); and Setting: primary care or hospital. The results were synthesized by reporting study characteristics and ROBs and by random effects modeling of the difference of the logit area under the receiver operating characteristic curve of each non-LR model compared with the LR model for the same pregnancy outcomes. We also reported between-study heterogeneity by using τ2 and I2.

RESULTS

Of the 2093 records, we included 142 studies for the systematic review and 62 studies for a meta-analysis. Most prediction models used LR (92/142, 64.8%) and artificial neural networks (20/142, 14.1%) among non-LR algorithms. Only 16.9% (24/142) of studies had a low ROB. A total of 2 non-LR algorithms from low ROB studies significantly outperformed LR. The first algorithm was a random forest for preterm delivery (logit AUROC 2.51, 95% CI 1.49-3.53; I2=86%; τ2=0.77) and pre-eclampsia (logit AUROC 1.2, 95% CI 0.72-1.67; I2=75%; τ2=0.09). The second algorithm was gradient boosting for cesarean section (logit AUROC 2.26, 95% CI 1.39-3.13; I2=75%; τ2=0.43) and gestational diabetes (logit AUROC 1.03, 95% CI 0.69-1.37; I2=83%; τ2=0.07).

CONCLUSIONS

Prediction models with the best performances across studies were not necessarily those that used LR but also used random forest and gradient boosting that also performed well. We recommend a reanalysis of existing LR models for several pregnancy outcomes by comparing them with those algorithms that apply standard guidelines.

TRIAL REGISTRATION

PROSPERO (International Prospective Register of Systematic Reviews) CRD42019136106; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=136106.

Technology trends and applications of deep learning in ultrasonography: image quality enhancement, diagnostic support, and improving workflow efficiency

In this review of the most recent applications of deep learning to ultrasound imaging, the architectures of deep learning networks are briefly explained for the medical imaging applications of classification, detection, segmentation, and generation. Ultrasonography applications for image processing and diagnosis are then reviewed and summarized, along with some representative imaging studies of the breast, thyroid, heart, kidney, liver, and fetal head. Efforts towards workflow enhancement are also reviewed, with an emphasis on view recognition, scanning guide, image quality assessment, and quantification and measurement. Finally some future prospects are presented regarding image quality enhancement, diagnostic support, and improvements in workflow efficiency, along with remarks on hurdles, benefits, and necessary collaborations.