Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology

WFUMB Commentary Paper on Artificial intelligence in Medical Ultrasound Imaging

Artificial intelligence (AI) is defined as the theory and development of computer systems able to perform tasks normally associated with human intelligence. At present, AI has been widely used in a variety of ultrasound tasks, including in point-of-care ultrasound, echocardiography, and various diseases of different organs. However, the characteristics of ultrasound, compared to other imaging modalities, such as computed tomography (CT) and magnetic resonance imaging (MRI), poses significant additional challenges to AI. Application of AI can not only reduce variability during ultrasound image acquisition, but can standardize these interpretations and identify patterns that escape the human eye and brain. These advances have enabled greater innovations in ultrasound AI applications that can be applied to a variety of clinical settings and disease states. Therefore, The World Federation of Ultrasound in Medicine and Biology (WFUMB) is addressing the topic with a brief and practical overview of current and potential future AI applications in medical ultrasound, as well as discuss some current limitations and future challenges to AI implementation.

Current Developments from Silicon Valley - How Artificial Intelligence is Changing Gynecology and Obstetrics

Artificial intelligence (AI) has become an omnipresent topic in the media. Lively discussions are being held on how AI could revolutionize the global healthcare landscape. The development of innovative AI models, including in the medical sector, is increasingly dominated by large high-tech companies. As a global technology epicenter, Silicon Valley hosts many of these technological giants which are muscling their way into healthcare provision with their advanced technologies. The annual conference of the American College of Obstetrics and Gynecology (ACOG) was held in San Francisco from 17 - 19 May 2024. ACOG celebrated its AI premier, hosting two sessions on current AI topics in gynecology at their annual conference. This paper provides an overview of the topics discussed and permits an insight into the thinking in Silicon Valley, showing how technology companies grow and fail there and examining how our American colleagues perceive increased integration of AI in gynecological and obstetric care. In addition to the classification of various, currently popular AI terms, the article also presents three areas where artificial intelligence is being used in gynecology and looks at the current developmental status in the context of existing obstacles to implementation and the current digitalization status of the German healthcare system.

Application of artificial intelligence in VSD prenatal diagnosis from fetal heart ultrasound images

BACKGROUND

Developing a combined artificial intelligence (AI) and ultrasound imaging to provide an accurate, objective, and efficient adjunctive diagnostic approach for fetal heart ventricular septal defects (VSD).

METHODS

1,451 fetal heart ultrasound images from 500 pregnant women were comprehensively analyzed between January 2016 and June 2022. The fetal heart region was manually labeled and the presence of VSD was discriminated by experts. The principle of five-fold cross-validation was followed in the training set to develop the AI model to assist in the diagnosis of VSD. The model was evaluated in the test set using metrics such as mAP@0.5, precision, recall, and F1 score. The diagnostic accuracy and inference time were also compared with junior doctors, intermediate doctors, and senior doctors.

RESULTS

The mAP@0.5, precision, recall, and F1 scores for the AI model diagnosis of VSD were 0.926, 0.879, 0.873, and 0.88, respectively. The accuracy of junior doctors and intermediate doctors improved by 6.7% and 2.8%, respectively, with the assistance of this system.

CONCLUSIONS

This study reports an AI-assisted diagnostic method for VSD that has a high agreement with manual recognition. It also has a low number of parameters and computational complexity, which can also improve the diagnostic accuracy and speed of some physicians for VSD.

Awareness, Training, and Perceived Needs of Gynecologists in Interpreting Basic Imaging Studies: A Cross-Sectional Survey in Saudi Arabia

BACKGROUND

Gynecologists often rely on imaging studies such as ultrasound, CT, and MRI for clinical decision-making, yet limited training in interpreting these studies may affect their confidence and ability to make timely diagnoses. This study aimed to assess the awareness, training and perceived needs of gynecologists in Saudi Arabia regarding the interpretation of basic imaging studies.

METHODS

A cross-sectional survey was conducted among 200 gynecologists practicing in Saudi Arabia. Participants were recruited using convenience sampling, and data were collected through an online questionnaire that assessed demographics, imaging knowledge, training history, perceived barriers, and interest in further education. Descriptive statistics were used to analyze the data.

RESULTS

The majority of respondents (44%) reported interpreting imaging studies occasionally, with ultrasound being the most commonly interpreted modality (63%). However, only 29% had received formal training in imaging studies, and 74% felt their training was insufficient. Most respondents (82%) expressed interest in additional training, particularly in ultrasound interpretation and emergency imaging. Key barriers to effective imaging interpretation included lack of training (43%) and reliance on radiology reports (24%). Nearly half (45%) of participants noted that delays in radiology reports affected their clinical decision-making.

CONCLUSIONS

This study reveals significant gaps in imaging interpretation training among gynecologists in Saudi Arabia, with a high demand for further education in basic imaging modalities. Addressing these gaps through structured training programs could improve gynecologists' confidence and clinical decision-making, ultimately leading to better patient care outcomes.

Fetal Face: Enhancing 3D Ultrasound Imaging by Postprocessing With AI Applications: Myth, Reality, or Legal Concerns?

The use of artificial intelligence (AI) platforms is revolutionizing the performance in managing metadata and big data. Medicine is another field where AI is spreading. However, this technological advancement is not amenable to errors or fraudulent misconducts. International organization and recently the European Union have released principles and recommendations for an appropriate use of AI in healthcare. In prenatal ultrasound diagnosis, the use of AI in daily practice is having a revolutionary impact. Notwithstanding, the diagnostic enhancement should be regulated, and AI applications should be developed to guarantee correct imaging acquisition and further postprocessing.

Internet-Based Abnormal Chromosomal Diagnosis During Pregnancy Using a Noninvasive Innovative Approach to Detecting Chromosomal Abnormalities in the Fetus: Scoping Review

BACKGROUND

Chromosomal abnormalities are genetic disorders caused by chromosome errors, leading to developmental delays, birth defects, and miscarriages. Currently, invasive procedures such as amniocentesis or chorionic villus sampling are mostly used, which carry a risk of miscarriage. This has led to the need for a noninvasive and innovative approach to detect and prevent chromosomal abnormalities during pregnancy.

OBJECTIVE

This review aims to describe and appraise the potential of internet-based abnormal chromosomal preventive measures as a noninvasive approach to detecting and preventing chromosomal abnormalities during pregnancy.

METHODS

A thorough review of existing literature and research on chromosomal abnormalities and noninvasive approaches to prenatal diagnosis and therapy was conducted. Electronic databases such as PubMed, Google Scholar, ScienceDirect, CENTRAL, CINAHL, Embase, OVID MEDLINE, OVID PsycINFO, Scopus, ACM, and IEEE Xplore were searched for relevant studies and articles published in the last 5 years. The keywords used included chromosomal abnormalities, prenatal diagnosis, noninvasive, and internet-based, and diagnosis.

RESULTS

The review of literature revealed that internet-based abnormal chromosomal diagnosis is a potential noninvasive approach to detecting and preventing chromosomal abnormalities during pregnancy. This innovative approach involves the use of advanced technology, including high-resolution ultrasound, cell-free DNA testing, and bioinformatics, to analyze fetal DNA from maternal blood samples. It allows early detection of chromosomal abnormalities, enabling timely interventions and treatment to prevent adverse outcomes. Furthermore, with the advancement of technology, internet-based abnormal chromosomal diagnosis has emerged as a safe alternative with benefits including its cost-effectiveness, increased accessibility and convenience, potential for earlier detection and intervention, and ethical considerations.

CONCLUSIONS

Internet-based abnormal chromosomal diagnosis has the potential to revolutionize prenatal care by offering a safe and noninvasive alternative to invasive procedures. It has the potential to improve the detection of chromosomal abnormalities, leading to better pregnancy outcomes and reduced risk of miscarriage. Further research and development in this field is needed to make this approach more accessible and affordable for pregnant women.

AI-enabled workflow for automated classification and analysis of feto-placental Doppler images

Introduction

Extraction of Doppler-based measurements from feto-placental Doppler images is crucial in identifying vulnerable new-borns prenatally. However, this process is time-consuming, operator dependent, and prone to errors.

Methods

To address this, our study introduces an artificial intelligence (AI) enabled workflow for automating feto-placental Doppler measurements from four sites (i.e., Umbilical Artery (UA), Middle Cerebral Artery (MCA), Aortic Isthmus (AoI) and Left Ventricular Inflow and Outflow (LVIO)), involving classification and waveform delineation tasks. Derived from data from a low- and middle-income country, our approach's versatility was tested and validated using a dataset from a high-income country, showcasing its potential for standardized and accurate analysis across varied healthcare settings.

Results

The classification of Doppler views was approached through three distinct blocks: (i) a Doppler velocity amplitude-based model with an accuracy of 94%, (ii) two Convolutional Neural Networks (CNN) with accuracies of 89.2% and 67.3%, and (iii) Doppler view- and dataset-dependent confidence models to detect misclassifications with an accuracy higher than 85%. The extraction of Doppler indices utilized Doppler-view dependent CNNs coupled with post-processing techniques. Results yielded a mean absolute percentage error of 6.1 ± 4.9% (n = 682), 1.8 ± 1.5% (n = 1,480), 4.7 ± 4.0% (n = 717), 3.5 ± 3.1% (n = 1,318) for the magnitude location of the systolic peak in LVIO, UA, AoI and MCA views, respectively.

Conclusions

The developed models proved to be highly accurate in classifying Doppler views and extracting essential measurements from Doppler images. The integration of this AI-enabled workflow holds significant promise in reducing the manual workload and enhancing the efficiency of feto-placental Doppler image analysis, even for non-trained readers.

Innovations in ultrasound training in obstetrics

INTRODUCTION

Ultrasound technology is critical in obstetrics, enabling detailed examination of the fetus and maternal anatomy. However, increasing complexity demands specialised training to maximise its potential. This study explores innovative approaches to ultrasound training in obstetrics, focussing on enhancing diagnostic skills and patient safety.

METHODS

This review examines recent innovations in ultrasound training, including competency-based medical education (CBME), simulation technologies, technology-based resources, artificial intelligence (AI), and online-learning platforms. Traditional training methods such as theoretical learning, practical experience, and peer learning are also discussed to provide a comprehensive view of current practises.

RESULTS

Innovations in ultrasound training include the use of high-fidelity simulators, virtual reality (VR), augmented reality (AR), and hybrid-learning platforms. Simulation technologies offer reproducibility, risk-free learning, diverse scenarios, and immediate feedback. AI and machine learning facilitate personalised-learning paths, real-time feedback, and automated-image analysis. Online-learning platforms and e-learning methods provide flexible, accessible, and cost-effective education. Gamification enhances learning motivation and engagement through educational games and virtual competitions.

DISCUSSION

The integration of innovative technologies in ultrasound training significantly improves diagnostic skills, learner confidence, and patient safety. However, challenges such as high costs, the need for comprehensive instructor training, and integration into existing programs must be addressed. Standardisation and certification ensure high-quality and consistent training. Future developments in AI, VR, and 3D printing promise further advancements in ultrasound education.

CONCLUSION

Innovations in ultrasound training in obstetrics offer significant improvements in medical education and patient care. The successful implementation and continuous development of these technologies are crucial to meet the growing demands of modern obstetrics.

Advancements in Artificial Intelligence for Fetal Neurosonography: A Comprehensive Review

The detailed sonographic assessment of the fetal neuroanatomy plays a crucial role in prenatal diagnosis, providing valuable insights into timely, well-coordinated fetal brain development and detecting even subtle anomalies that may impact neurodevelopmental outcomes. With recent advancements in artificial intelligence (AI) in general and medical imaging in particular, there has been growing interest in leveraging AI techniques to enhance the accuracy, efficiency, and clinical utility of fetal neurosonography. The paramount objective of this focusing review is to discuss the latest developments in AI applications in this field, focusing on image analysis, the automation of measurements, prediction models of neurodevelopmental outcomes, visualization techniques, and their integration into clinical routine.

Diagnostic Accuracy of an Integrated AI Tool to Estimate Gestational Age From Blind Ultrasound Sweeps

Importance

Accurate assessment of gestational age (GA) is essential to good pregnancy care but often requires ultrasonography, which may not be available in low-resource settings. This study developed a deep learning artificial intelligence (AI) model to estimate GA from blind ultrasonography sweeps and incorporated it into the software of a low-cost, battery-powered device.

Objective

To evaluate GA estimation accuracy of an AI-enabled ultrasonography tool when used by novice users with no prior training in sonography.

Design, Setting, and Participants

This prospective diagnostic accuracy study enrolled 400 individuals with viable, single, nonanomalous, first-trimester pregnancies in Lusaka, Zambia, and Chapel Hill, North Carolina. Credentialed sonographers established the "ground truth" GA via transvaginal crown-rump length measurement. At random follow-up visits throughout gestation, including a primary evaluation window from 14 0/7 weeks' to 27 6/7 weeks' gestation, novice users obtained blind sweeps of the maternal abdomen using the AI-enabled device (index test) and credentialed sonographers performed fetal biometry with a high-specification machine (study standard).

Main Outcomes and Measures

The primary outcome was the mean absolute error (MAE) of the index test and study standard, which was calculated by comparing each method's estimate to the previously established GA and considered equivalent if the difference fell within a prespecified margin of ±2 days.

Results

In the primary evaluation window, the AI-enabled device met criteria for equivalence to the study standard, with an MAE (SE) of 3.2 (0.1) days vs 3.0 (0.1) days (difference, 0.2 days [95% CI, -0.1 to 0.5]). Additionally, the percentage of assessments within 7 days of the ground truth GA was comparable (90.7% for the index test vs 92.5% for the study standard). Performance was consistent in prespecified subgroups, including the Zambia and North Carolina cohorts and those with high body mass index.

Conclusions and Relevance

Between 14 and 27 weeks' gestation, novice users with no prior training in ultrasonography estimated GA as accurately with the low-cost, point-of-care AI tool as credentialed sonographers performing standard biometry on high-specification machines. These findings have immediate implications for obstetrical care in low-resource settings, advancing the World Health Organization goal of ultrasonography estimation of GA for all pregnant people.

Trial Registration

ClinicalTrials.gov Identifier: NCT05433519.

Quality of ChatGPT-Generated Therapy Recommendations for Breast Cancer Treatment in Gynecology

Introduction: Artificial intelligence (AI) is revolutionizing medical workflows, with self-learning systems like ChatGPT showing promise in therapy recommendations. Our study evaluated ChatGPT's performance in suggesting treatments for 30 breast cancer cases. AI's role in healthcare is expanding, particularly with tools like ChatGPT becoming accessible. However, understanding its limitations is vital for safe implementation. Material and Methods: We used 30 breast cancer cases from our medical board, assessing ChatGPT's suggestions. The input was standardized, incorporating relevant patient details and treatment options. ChatGPT's output was evaluated by oncologists based on a given questionnaire. Results: Treatment recommendations by ChatGPT were overall rated sufficient with minor limitations by the oncologists. The HER2 treatment category was the best-rated therapy option, with the most accurate recommendations. Primary cases received more accurate recommendations, especially regarding chemotherapy. Conclusions: While ChatGPT demonstrated potential, difficulties were shown in intricate cases and postoperative scenarios. Challenges arose in offering chronological treatment sequences and partially lacked precision. Refining inputs, addressing ethical intricacies, and ensuring chronological treatment suggestions are essential. Ongoing research is vital to improving AI's accuracy, balancing AI-driven suggestions with expert insights and ensuring safe and reliable AI integration into patient care.

How automated techniques ease functional assessment of the fetal heart: Applicability of two-dimensional speckle-tracking echocardiography for comprehensive analysis of global and segmental cardiac deformation using fetalHQ®

BACKGROUND

Prenatal echocardiographic assessment of fetal cardiac function has become increasingly important. Fetal two-dimensional speckle-tracking echocardiography (2D-STE) allows the determination of global and segmental functional cardiac parameters. Prenatal diagnostics is relying increasingly on artificial intelligence, whose algorithms transform the way clinicians use ultrasound in their daily workflow. The purpose of this study was to demonstrate the feasibility of whether less experienced operators can handle and might benefit from an automated tool of 2D-STE in the clinical routine.

METHODS

A total of 136 unselected, normal, singleton, second- and third-trimester fetuses with normofrequent heart rates were examined by targeted ultrasound. 2D-STE was performed separately by beginner and expert semiautomatically using a GE Voluson E10 (FetalHQ®, GE Healthcare, Chicago, IL). Several fetal cardiac parameters were calculated (end-diastolic diameter [ED], sphericity index [SI], global longitudinal strain [EndoGLS], fractional shortening [FS]) and assigned to gestational age (GA). Bland-Altman plots were used to test agreement between both operators.

RESULTS

The mean maternal age was 33 years, and the mean maternal body mass index prior to pregnancy was 24.78 kg/m2. The GA ranged from 16.4 to 32.0 weeks (average 22.9 weeks). Averaged endoGLS value of the beginner was -18.57% ± 6.59 percentage points (pp) for the right and -19.58% ± 5.63 pp for the left ventricle, that of the expert -14.33% ± 4.88 pp and -16.37% ± 5.42 pp. With increasing GA, right ventricular endoGLS decreased slightly while the left ventricular was almost constant. The statistical analysis for endoGLS showed a Bland-Altman-Bias of -4.24 pp ± 8.06 pp for the right and -3.21 pp ± 7.11 pp for the left ventricle. The Bland-Altman-Bias of the ED in both ventricles in all analyzed segments ranged from -.49 mm ± 1.54 mm to -.10 mm ± 1.28 mm, that for FS from -.33 pp ± 11.82 pp to 3.91 pp ± 15.56 pp and that for SI from -.38 ± .68 to -.15 ± .45.

CONCLUSIONS

Between both operators, our data indicated that 2D-STE analysis showed excellent agreement for cardiac morphometry parameters (ED and SI), and good agreement for cardiac function parameters (EndoGLS and FS). Due to its complexity, the application of fetal 2D-STE remains the domain of scientific-academic perinatal ultrasound and should be placed preferably in the hands of skilled operators. At present, from our perspective, an implementation into clinical practice "on-the-fly" cannot be recommended.

Clinical Ultrasound Applications in Obstetrics and Gynecology in the Year 2024

Ultrasound imaging stands as a fundamental technology in the realms of obstetrics and gynecology, utilizing high-frequency sound waves to create detailed images of the internal structures of the body [...].

Revolutionizing Women's Health: A Comprehensive Review of Artificial Intelligence Advancements in Gynecology

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.

Evaluating the Efficacy of ChatGPT in Navigating the Spanish Medical Residency Entrance Examination (MIR): Promising Horizons for AI in Clinical Medicine

The rapid progress in artificial intelligence, machine learning, and natural language processing has led to increasingly sophisticated large language models (LLMs) for use in healthcare. This study assesses the performance of two LLMs, the GPT-3.5 and GPT-4 models, in passing the MIR medical examination for access to medical specialist training in Spain. Our objectives included gauging the model's overall performance, analyzing discrepancies across different medical specialties, discerning between theoretical and practical questions, estimating error proportions, and assessing the hypothetical severity of errors committed by a physician.

MATERIAL AND METHODS

We studied the 2022 Spanish MIR examination results after excluding those questions requiring image evaluations or having acknowledged errors. The remaining 182 questions were presented to the LLM GPT-4 and GPT-3.5 in Spanish and English. Logistic regression models analyzed the relationships between question length, sequence, and performance. We also analyzed the 23 questions with images, using GPT-4's new image analysis capability.

RESULTS

GPT-4 outperformed GPT-3.5, scoring 86.81% in Spanish (p < 0.001). English translations had a slightly enhanced performance. GPT-4 scored 26.1% of the questions with images in English. The results were worse when the questions were in Spanish, 13.0%, although the differences were not statistically significant (p = 0.250). Among medical specialties, GPT-4 achieved a 100% correct response rate in several areas, and the Pharmacology, Critical Care, and Infectious Diseases specialties showed lower performance. The error analysis revealed that while a 13.2% error rate existed, the gravest categories, such as "error requiring intervention to sustain life" and "error resulting in death", had a 0% rate.

CONCLUSIONS

GPT-4 performs robustly on the Spanish MIR examination, with varying capabilities to discriminate knowledge across specialties. While the model's high success rate is commendable, understanding the error severity is critical, especially when considering AI's potential role in real-world medical practice and its implications for patient safety.