Gelan Ayana

Background: Preterm birth (PTB), defined as delivery before 37 completed weeks of gestation, remains a major cause of neonatal mortality and long-term morbidity worldwide. Conventional risk assessment strategies, including cervical length measurement and biomarker-based screening, have shown limited predictive performance. Machine learning (ML) may improve PTB prediction by integrating heterogeneous clinical data and identifying complex risk patterns. Methods: A structured narrative review was conducted by systematically searching PubMed, Web of Science, Scopus, IEEE Xplore, and Google Scholar for studies published between January 2010 and April 2024. Studies were screened according to predefined eligibility criteria, and 14 studies evaluating ML models for PTB prediction using human pregnancy datasets were included in the final narrative synthesis. Data on study characteristics, predictor variables, ML methods, validation strategies, and model performance were extracted and synthesized narratively. Results: A total of 14 studies met the predefined eligibility criteria and were included in the final narrative synthesis. ML models generally performed better when they used longitudinal electronic health records (EHRs), repeated measurements, or richer maternal clinical histories. Ensemble approaches such as random forest, gradient boosting, and stacking models, as well as deep learning (DL) methods including artificial neural networks (ANNs), recurrent neural networks (RNNs), and long short-term memory networks (LSTMs), were frequently among the best-performing models. Across the included studies, previous PTB, cervical length, maternal age, hypertensive disorders, diabetes, and body mass index (BMI) emerged as the most consistently reported predictors of ML model performance. However, the evidence was highly heterogeneous in data sources, outcome definitions, validation methods, and performance reporting. External validation, calibration assessment, and real-world clinical evaluation were limited. Conclusion: ML shows considerable promise for improving early PTB risk stratification, but clinical translation will depend on early prediction, interpretable outputs, and robust external validation. Although ML models generally demonstrated promising predictive performance, interpretation of these findings is limited by methodological heterogeneity and inconsistent external validation across studies. Future studies should prioritize standardized reporting, multicenter external validation, calibration assessment, explainability, and prospective implementation studies to facilitate routine clinical adoption.

Acute flaccid paralysis (AFP) surveillance in Ethiopia remains limited due to hard-to-reach populations, restricted healthcare access, low public awareness, and persistent underreporting within community-based surveillance (CBS) systems. Contributing to this are inadequate training of community reporters, communication barriers, and cultural beliefs and misinformation that further hinder timely case detection. Here, AFP Assistant, a multilingual large language model powered chatbot, is developed to strengthen CBS through improved health communication and real-time reporting. The system supports Amharic, Afaan Oromo, and English to enhance accessibility and inclusivity. A curated dataset derived from literature, guidelines, and community interviews was translated and validated by experts to ensure clinical and cultural accuracy. The system integrates supervised fine-tuning with retrieval-augmented generation (RAG) to deliver accurate, context-aware responses. Trained on 468 question–answer pairs using pretrained Gemini 2.5-flash, the model achieved an accuracy of 0.90 and a loss of 0.31. The RAG framework improved retrieval relevance, grounding, and response efficiency. Evaluation through in-house testing and human annotation using a structured rubric demonstrated robust performance across tasks and languages, while user feedback confirmed usability and relevance. These findings highlight the potential of AI-driven chatbots to enhance AFP surveillance and equitable health communication in low-resource settings.

Cardiovascular diseases (CVDs) are a leading cause of morbidity and mortality globally, with low-resource settings, including Ethiopia facing challenges due to limited early diagnostic services. AI-powered electrocardiography (ECG) interpretation has the potential to improve diagnostic accuracy, decentralize care, and support timely clinical decisions, but evidence on healthcare providers’ perspectives and adoption determinants is limited. This exploratory descriptive qualitative study employed 31 in-depth interviews with healthcare providers. Healthcare providers (cardiologists, internists, cardiac and critical care nurses, critical care specialists, and general practitioners) were purposively selected through maximum variation sampling from ten hospitals in four regions of Ethiopia. Data were transcribed verbatim, coded inductively, and analyzed thematically. The data analysis identified six themes: perceived benefit of AI-powered ECG interpretation CDSS, trust development, workflow integration, ethical concerns, functionality, and adoption determinants. Participants emphasized AI’s potential to enhance accessibility, consistency, and diagnostic accuracy while reducing subjectivity and unnecessary referrals. Acceptance relied on high accuracy, reliable data, and rigorous validation, with the technology seen as supportive rather than replacing clinicians. Material resources, human resource readiness, and leadership engagement were key factors for adoption. Recommendations included phased implementation, continuous training, and model expansion to ensure sustainability and clinical utility. The AI-powered ECG interpretation CDSS was viewed as a valuable adjunct for strengthening cardiovascular care in Ethiopia, highlighting the need for context-sensitive strategies, ethical safeguards, and multi-level system readiness for successful adoption.

Poliomyelitis is a severe viral infection that can lead to permanent paralysis or death, primarily affecting children under 15 years of age. Acute Flaccid Paralysis (AFP) surveillance is essential for global polio eradication efforts, enabling the rapid detection, reporting, and investigation of suspected cases. Implementing various technological solutions has significantly advanced AFP surveillance, which is the gold standard for early polio case identification. These technologies include short message alert systems for better case reporting, the Open Data Kit (ODK) for efficient data collection and management, digital specimen tracking systems for improved sample handling, geospatial mapping tools for monitoring surveillance coverage, and awareness campaigns to engage healthcare workers and communities. Despite these advancements, there remains a notable absence of comprehensive analyses evaluating the cumulative progress, challenges, and policy implications of AFP surveillance. This study presents a targeted literature review of English-language publications from 2002 to 2024, focusing on challenges, technological advancements, and practical recommendations for AFP surveillance. Only empirically grounded, methodologically sound studies centered on AFP within health systems were included, with preference for research conducted in polio-endemic countries. Studies lacking methodological rigor or direct relevance were excluded. The review highlights how digital reporting platforms, specimen tracking, geospatial monitoring, and awareness campaigns have strengthened surveillance operations. At the same time, it identifies persisting weaknesses in surveillance methodologies and gaps in coverage, underscoring the need for further innovation and integration into health systems. By synthesizing evidence on the latest developments, this review addresses a critical gap in the literature and provides actionable recommendations to improve AFP surveillance. The findings inform both practice and policy by identifying innovative solutions and future directions, ultimately contributing to stronger global health surveillance systems and advancing polio eradication strategies.

Background/Objective: The global spread of Monkeypox (Mpox) has highlighted the urgent need for rapid, accurate diagnostic tools. Traditional methods like polymerase chain reaction (PCR) are resource-intensive, while skin image-based detection offers a promising alternative. This study evaluates the effectiveness of vision transformers (ViTs) for automated Mpox detection. Methods: By fine-tuning a pre-trained ViT model on an Mpox lesion image dataset, a robust ViT-based transfer learning (TL) model was created. Performance was assessed relative to convolutional neural network (CNN)-based TL models and ViT models trained from scratch across key metrics: accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). Furthermore, a transferability measure was utilized to assess the effectiveness of feature transfer to Mpox images. Results: The results show that the ViT model outperformed a CNN, achieving an AUC of 0.948 and an accuracy of 0.942 with a p-value of less than 0.05 across all metrics, highlighting its potential for accurate and scalable Mpox detection. Moreover, the ViT models yielded a better hypothesis margin-based transferability measure, highlighting its effectiveness in transferring useful learning weights to Mpox images. Gradient-weighted Class Activation Mapping (Grad-CAM) visualizations also confirmed that the ViT model attends to clinically relevant features, supporting its interpretability and reliability for diagnostic use. Conclusions: The results from this study suggest that ViT offers superior accuracy, making it a valuable tool for Mpox early detection in field settings, especially where conventional diagnostics are limited. This approach could support faster outbreak response and improved resource allocation in public health systems.

Acute flaccid paralysis (AFP) case surveillance is pivotal for the early detection of potential poliovirus, particularly in endemic countries such as Ethiopia. The community-based surveillance system implemented in Ethiopia has significantly improved AFP surveillance. However, challenges like delayed detection and disorganized communication persist. This work proposes a simple deep learning model for AFP surveillance, leveraging transfer learning on images collected from Ethiopia's community key informants through mobile phones. The transfer learning approach is implemented using a vision transformer model pretrained on the ImageNet dataset. The proposed model outperformed convolutional neural network-based deep learning models and vision transformer models trained from scratch, achieving superior accuracy, F1-score, precision, recall, and area under the receiver operating characteristic curve (AUC). It emerged as the optimal model, demonstrating the highest average AUC of 0.870 +/- 0.01. Statistical analysis confirmed the significant superiority of the proposed model over alternative approaches (P < 0.001). By bridging community reporting with health system response, this study offers a scalable solution for enhancing AFP surveillance in low-resource settings. The study is limited in terms of the quality of image data collected, necessitating future work on improving data quality. The establishment of a dedicated platform that facilitates data storage, analysis, and future learning can strengthen data quality. Nonetheless, this work represents a significant step toward leveraging artificial intelligence for community-based AFP surveillance from images, with substantial implications for addressing global health challenges and disease eradication strategies. (c) 2024 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Polio is a highly contagious viral disease that primarily affects children under 15, often leading to permanent paralysis, known as acute flaccid paralysis (AFP). AFP surveillance is essential for the eradication of polio, with community-based surveillance (CBS) playing a pivotal role in detecting and reporting cases. CBS improves the timeliness and accuracy of AFP detection, but challenges such as underreporting, delays, and low community awareness persist. Strategies involving use of mobile applications, awareness campaigns, and improvements in healthcare infrastructure were implemented to improve CBS of AFP. While numerous case studies from various countries illustrate the implementation of CBS, a comprehensive synthesis of these studies across diverse contexts is limited. This paper examines state-of-the-art CBS approaches for AFP, analyzing progress, challenges, and potential solutions. A targeted literature review of English-language studies published between 2004 and 2024 was conducted, focusing on the roles of communities, technological integration, and practical recommendations, while excluding studies that lacked methodological rigor or direct relevance. The review revealed that CBS has significantly advanced the global fight against polio by increasing community awareness, enabling earlier detection, and improving the reporting of AFP cases. However, issues such as security concerns, delayed reporting, low levels of community awareness, and underutilization of technology persist. This review recommends strengthening organizational structures, improving healthcare access, raising community awareness, and using technology for more efficient AFP surveillance. The implication of this work is beyond polio as it offers a comprehensive framework for integrating disease surveillance, technology and community involvement to strengthen public health strategies and build robust health systems.