Amita Nanda1*, Pragati Trigunait2, Arvind Kumar Yadav3, Ratan Pal4, Pragya Dixit5 and Girish Singh Kshatriya6
Department of MS Obstetrics and Gynecology, ABVIMS & RML Hospital, New Delhi, India
Assistant Professor, Department of MS Obstetrics and Gynecology, All India Institute of Medical Sciences, Mangalagiri, Andhra Pradesh, India
Assistant Professor, Department of Transfusion Medicine, Armed Forces Transfusion Centre, New Delhi, India
NRI Academy of Medical Sciences, Guntur, Andhra Pradesh, India
Assistant Professor, Department of MD Transfusion Medicine, Government Medical College, Jagdalpur, Chhattisgarh, India
Received: 01 May 2024; Processed: 04 June 2024; Accepted: 08 June 2024
Citation: Nanda, Amita, et al. “Advancements in Artificial Intelligence and Machine Learning for Early Detection and Transfusion Support in Postpartum Haemorrhage.” J Gynecol Matern Health (2024): 106. DOI: 10.59462/JGMH.2.1.106
Copyright: © 2024 Nanda A. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Postpartum Hemorrhage (PPH) remains a leading cause of maternal mortality and morbidity worldwide, responsible for approximately 25% of all maternal deaths globally [1]. A commonly used definition for PPH is a blood loss of over 500 ml within 24 hours after childbirth and poses significant challenges in both diagnosis and management [2]. The rapid identification and timely intervention are critical in preventing severe outcomes and improving maternal health. Advancements in artificial intelligence (AI) and machine learning (ML) have begun to transform various aspects of healthcare, offering new horizons in patient diagnostics and treatment strategies. In the realm of obstetrics, AI and ML technologies are increasingly being explored for their potential to predict and detect early signs of complications such as PPH. These technologies are capable of analyzing large datasets to identify patterns that may not be immediately apparent to human observers, thus providing a crucial advantage in anticipating and preventing severe clinical events [3].
The integration of AI into healthcare has been met with enthusiasm for its potential to enhance diagnostic accuracy, personalize treatment, and optimize patient outcomes. Specifically, in the context of PPH, AI algorithms can be employed to analyze risk factors, historical health data, and real-time vital signs to predict the likelihood of PPH occurring. This predictive capability is essential in obstetric care, where quick decision-making can substantially alter outcomes [4]. AI and ML are not just tools for prediction; they also play a crucial role in monitoring. Real-time monitoring systems equipped with ML algorithms can continuously assess a patient’s condition and alert healthcare providers to subtle changes that may precede a PPH event. Such systems are invaluable in settings where the patient-toprovider ratio is high, and continuous human monitoring is challenging [5].
Despite the promising advances, the adoption of AI and ML in clinical settings faces several challenges. The accuracy of these technologies greatly depends on the quality and quantity of the data they are trained on. Inaccuracies in data or biases introduced during the machine learning process can lead to less reliable outputs, which in critical fields like obstetrics, can have serious repercussions [6]. Moreover, integrating these advanced technologies into existing healthcare workflows and systems poses logistical and ethical considerations that must be addressed to facilitate their effective use. While the landscape of obstetric care is being reshaped by innovations in AI and ML, significant work remains to be done to ensure these tools are reliable, effective, and equitable. As these technologies evolve, they hold the promise of significantly improving the detection and management of PPH, ultimately enhancing maternal safety and health outcomes.
Literature search strategy
The literature search was conducted through various academic databases including PubMed, Embase, and Web of Science to ensure comprehensive coverage of recent studies on AI and ML applications in the early detection of postpartum hemorrhage (PPH). The search strategy was tailored to include a combination of keywords such as “postpartum hemorrhage”, “AI”, “ML”, “early detection”, and “predictive modeling”. Additionally, other literature sources and conference proceedings were reviewed to capture emerging research and innovations that have not yet been published in peer-reviewed journals.
Data extraction and analysis
Relevant articles were selected based on specific inclusion criteria: studies must have utilized AI or ML in the context of PPH. Exclusion criteria included non-English articles and those not involving empirical research. Data extracted from the articles included author names, publication year, study objectives, methodologies, key findings, and the type of ML algorithms used. The synthesis of extracted data involved qualitative comparisons of different ML approaches and their effectiveness in predicting PPH.
AI and ML technologies in PPH detection
Recent research has explored various AI and ML methodologies to improve the detection and management of postpartum hemorrhage (PPH), including aspects directly related to transfusion support. A key study demonstrated the utility of machine learning in analyzing factors like fibrinogen levels at the time of PPH diagnosis to predict the severity of hemorrhage, which is crucial for managing transfusion needs efficiently. This approach underscores the potential of predictive modeling in assessing hemorrhage severity and preparing necessary transfusion responses preemptively [7]. Moreover, another significant study focused on PPH management within a perinatal network, analyzing data from a vast number of cases to identify trends and risk factors associated with PPH that requires transfusion. This study highlighted the importance of tailored management strategies that could benefit from AI-driven insights, improving outcomes through better prediction and preparedness for severe cases [8].
These studies collectively illustrate the promising integration of AI and ML in enhancing predictive accuracy and operational efficiency in managing PPH, particularly concerning optimizing transfusion practices and improving maternal health outcomes. Predictive Modeling Enhanced for Transfusion Support has also been suggested. It includes advanced predictive models like decision trees, support vector machines (SVMs), and neural networks are further refined to include parameters specifically relevant to transfusion needs. By incorporating variables such as previous transfusion history, blood type compatibility, and real-time hemoglobin levels, these models not only predict the occurrence of PPH but also anticipate the potential need for blood transfusions, thereby streamlining the preparation process in high-risk cases.
Risk stratification with transfusion readiness
AI-driven risk stratification tools now integrate transfusion readiness as a critical parameter. By evaluating the risk of PPH alongside indicators for transfusion requirements (like baseline anemia or known coagulopathies), these tools can alert clinical teams to prepare blood products well in advance. This proactive approach ensures that blood transfusions, when needed, are timely and matched to the patient’s specific blood type and condition, reducing the risk of adverse reactions.
Real-time monitoring systems linking to transfusion services
Real-time monitoring systems that track vital signs and other indicators are now equipped to trigger automatic alerts to hospital transfusion services when a PPH event is detected. These systems use continuous data analysis to monitor blood loss and vital signs deterioration, activating protocols for immediate blood product support. This integration facilitates a seamless transition from detection to treatment, crucial for the effective management of PPH.
Implementation challenges of AI in PPH detection and management
Data quality and availability: The quality and comprehensiveness of the data used to train AI models present significant challenges. Effective AI applications require high-quality data that accurately represents the diverse patient population it serves. However, healthcare data often suffer from issues like missing values, inconsistencies, and bias, which can adversely affect the performance of AI systems. Additionally, the availability of comprehensive datasets, which are crucial for training robust models, is often limited due to privacy concerns and logistical constraints [9].
Integration with clinical workflows: Integrating AI tools into existing clinical workflows poses another significant challenge. These tools must be designed to fit seamlessly into the electronic health records (EHR) systems that hospitals use. This integration requires careful planning to ensure that AI tools enhance, rather than disrupt, the workflow of healthcare professionals. Furthermore, these tools need to be compatible with various software systems used across different healthcare facilities, which often have unique configurations and requirements [10].
Acceptance and trust by healthcare professionals: The acceptance and trust of healthcare professionals in AI technologies are critical for their successful implementation. While AI can enhance decision-making and efficiency, there can be resistance due to fears of job displacement, concerns about the accuracy of AI recommendations, and a lack of understanding of how AI tools work. To build trust, AI systems need to be transparent in their operations and for their recommendations to be interpretable by human users. Training and education are also crucial in helping healthcare providers understand and effectively use AI tools [11].
Emerging technologies: Advancements in AI and ML continue to evolve, with several emerging technologies poised to enhance PPH detection significantly. Technologies such as federated learning can enable AI models to learn from vast amounts of decentralized data while maintaining privacy. This approach could improve model generalizability across different populations. Additionally, edge computing can facilitate real-time data analysis directly at the point of care, potentially speeding up the detection and response to PPH incidents. Integration of AI with wearable technologies could also provide continuous monitoring of physiological parameters, offering early warnings of potential complications.
Research needs: Current research has demonstrated the potential of AI in PPH management, yet gaps remain that need addressing. There is a need for studies that evaluate the long-term impacts of AI interventions on maternal health outcomes. Research is also needed to address the variability in data quality across different health systems, which can affect AI performance. Furthermore, ethical considerations, including patient consent and data privacy, require ongoing research to develop guidelines that protect patient rights while enabling technological advancements.
This review highlighted the use of AI and ML in detecting and managing PPH and discussed the challenges related to data quality, integration into clinical workflows, and acceptance by healthcare professionals. It also introduced emerging technologies that could further enhance PPH detection. The integration of AI and ML into PPH management offers potential benefits such as improved prediction accuracy, personalized patient care, and optimized resource allocation. However, for these technologies to be effectively integrated into clinical practice, healthcare providers must be adequately trained, that the systems are transparent, and that they complement existing clinical judgment.
The future of AI and ML in improving maternal health outcomes looks promising, particularly in the realm of PPH management. As these technologies advance, they hold the potential not only to enhance healthcare delivery but also to transform it, making pregnancy and childbirth safer for women worldwide. Continuous innovation, coupled with rigorous research and ethical considerations, will be essential to realize the full potential of AI and ML in obstetrics.