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Hybridized artificial intelligence system for reducing neonatal mortality in Nigeria
Full text linkBackground
Neonatal diseases represent the leading cause of death in Nigeria, ranking the country second globally in neonatal mortality rates. Early and accurate diagnosis remains challenging, leading to delayed interventions and increased mortality.
Aim
To develop an artificial intelligence system capable of detecting multiple neonatal diseases using local datasets and advanced machine learning techniques to facilitate early intervention and reduce neonatal mortality in Southwest Nigeria.
Methods
Clinical records from 4,027 previously treated neonatal patients were collected from five tertiary hospitals across three Southwest Nigerian states. The dataset underwent comprehensive analysis, balancing using Synthetic Minority Over-sampling Technique (SMOTE), and preprocessing before training three deep learning models: Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), and a novel hybrid LSTM-ANN architecture. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics with rigorous subject-wise validation and statistical testing.
Results
The hybrid LSTM-ANN model demonstrated superior performance with 82 % accuracy, 88 % precision, 82 % recall, and 86 % F1-score, significantly outperforming both standalone ANN (80 % accuracy) and LSTM (77 % accuracy). Disease-specific classification revealed exceptional performance for sepsis (precision: 0.90, F1-score: 0.88), birth asphyxia (0.88, 0.85), jaundice (0.86, 0.83), and prematurity (0.82, 0.80). McNemar’s test confirmed significant hybrid superiority over ANN (χ2 = 12.45, p < 0.001) and LSTM (χ2 = 15.67, p < 0.001), whilst Friedman test (χ2 = 18.42, p < 0.001) validated the 5–6 % accuracy improvement.
Conclusion
The hybrid LSTM-ANN model establishes a valuable diagnostic tool for early neonatal disease detection. However, external validation and prospective clinical trials are necessary before clinical deployment
Artificial intelligence in clinical trials: A comprehensive review of opportunities, challenges, and future directions
Full text linkBackground
Clinical trials face unprecedented challenges including recruitment delays affecting 80% of studies, escalating costs exceeding $200 billion annually in pharmaceutical R&D, success rates below 12%, and data quality issues affecting 50% of datasets. Artificial intelligence (AI) offers transformative solutions to address these systemic inefficiencies across the clinical trial lifecycle.
Objective
To evaluate the current state, future potential, and implementation challenges of AI technologies in clinical trials, providing evidence-based guidance for responsible AI integration while maintaining patient safety and scientific integrity.
Method
Comprehensive narrative review following established guidelines for literature synthesis. Systematic search of PubMed, Embase, IEEE Xplore, and Google Scholar databases from January 2015 to December 2024. Data extraction and narrative synthesis organized thematically according to clinical trial lifecycle stages.
Results
Analysis of relevant studies demonstrated substantial AI benefits: patient recruitment tools improved enrollment rates by 65%, predictive analytics models achieved 85% accuracy in forecasting trial outcomes, and AI integration accelerated trial timelines by 30–50% while reducing costs by up to 40%. Digital biomarkers enabled continuous monitoring with 90% sensitivity for adverse event detection. However, significant implementation barriers emerged, including data interoperability challenges, regulatory uncertainty, algorithmic bias concerns, and limited stakeholder trust.
Conclusion
AI represents a transformative force in clinical research with proven capabilities to enhance efficiency, reduce costs, and improve patient outcomes. Realizing this potential requires addressing technical infrastructure limitations, developing explainable AI systems, establishing comprehensive regulatory frameworks, and fostering collaborative efforts between technology developers, clinical researchers, and regulatory agencies to ensure responsible implementation
Comparative Genomic Hybridization (CGH) in Genotoxicology: From the Basics to Modern Approaches.
No full textOver the past two decades, comparative genomic hybridization (CGH) and array CGH have become essential tools in clinical diagnostics, oncology, and toxicological risk assessment. Initially developed to identify chromosomal imbalances like copy number variations (CNVs) in tumor cells, these technologies have expanded into genotoxicology and toxicogenomics, exploring gene responses to toxic agents and their molecular mechanisms. As of 2024, new developments include integrating array CGH with next-generation sequencing (NGS), machine learning, and CRISPR-Cas9 genome editing, greatly improving precision. High-density CGH arrays now offer single-cell resolution, enabling the detection of cellular heterogeneity in toxic responses, while long-read sequencing facilitates the identification of complex genomic rearrangements. Recent innovations include combining CGH and toxicogenomics with organ-on-chip models for real-time, tissue-specific toxicological assessment. This has significantly improved the relevance of toxicological data for human health. However, while these advances are promising, array CGH remains costly and requires substantial data processing, driving the need for advanced bioinformatics tools. AI-driven predictive toxicology models are also gaining traction, correlating toxicogenomic profiles with clinical outcomes. Despite these advancements, the field still faces challenges, such as evolving regulatory guidelines and complex data interpretation, which hinder broader adoption and the full realization of CGH's potential in toxicology and risk assessment. [Abstract copyright: © 2026. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.
The Impact of Artificial Intelligence and Machine Learning in Organ Retrieval and Transplantation: A Comprehensive Review
Full text linkThis narrative review examines the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in organ retrieval and transplantation. AI and ML technologies enhance donor-recipient matching by integrating and analyzing complex datasets encompassing clinical, genetic, and demographic information, leading to more precise organ allocation and improved transplant success rates. In surgical planning, AI-driven image analysis automates organ segmentation, identifies critical anatomical features, and predicts surgical outcomes, aiding pre-operative planning and reducing intraoperative risks. Predictive analytics further enable personalized treatment plans by forecasting organ rejection, infection risks, and patient recovery trajectories, thereby supporting early intervention strategies and long-term patient management. AI also optimizes operational efficiency within transplant centers by predicting organ demand, scheduling surgeries efficiently, and managing inventory to minimize wastage, thus streamlining workflows and enhancing resource allocation. Despite these advancements, several challenges hinder the widespread adoption of AI and ML in organ transplantation. These include data privacy concerns, regulatory compliance issues, interoperability across healthcare systems, and the need for rigorous clinical validation of AI models. Addressing these challenges is essential to ensuring the reliable, safe, and ethical use of AI in clinical settings. Future directions for AI and ML in transplantation medicine include integrating genomic data for precision immunosuppression, advancing robotic surgery for minimally invasive procedures, and developing AI-driven remote monitoring systems for continuous post-transplantation care. Collaborative efforts among clinicians, researchers, and policymakers are crucial to harnessing the full potential of AI and ML, ultimately transforming transplantation medicine and improving patient outcomes while enhancing healthcare delivery efficiency
Exploring ableism and occupational therapy: Occupational therapy students’ perspectives
Full text linkAim:
To explore occupational therapy students’ perspectives on ableism and its implications for occupational therapy practice. This formed part of a wider study that also explored occupational therapy educators’ perspectives.
Method:
An online survey was used to collect students’ perspectives, using a mixture of Likert scales and open-ended questions.
Findings:
The sample comprised 56 occupational therapy students from the United Kingdom ( n = 36), United States of America ( n = 16) and Canada ( n = 4) enrolled in a mixture of undergraduate ( n = 13) and postgraduate ( n = 43) pre-registration degree programmes. Thirty-four percent of respondents perceived occupational therapy as inherently ableist. This rose to 50% after respondents were presented with a comprehensive definition of ableism. Students reported witnessing and/or experiencing ableism within education (63%) and practice placements (55%). Eighty-six percent of students recognised they may hold unconscious ableist views, and 96% agreed they would like more support to engage in disability studies.
Conclusion/Impact:
Findings indicated a potential link between understanding of ableism and students’ views that occupational therapy is ableist. Most students were aware of the potential they hold unconscious biases and welcomed support to engage further with disability studies. Further qualitative research is needed. Following this, systemic changes to address the harm of ableism can begin to be addressed
Artificial Intelligence in in-vitro fertilization (IVF): A New Era of Precision and Personalization in Fertility Treatments
Full text linkIn-vitro fertilization (IVF) has been a transformative advancement in assisted reproductive technology. However, success rates remain suboptimal, with only about one-third of cycles resulting in pregnancy and fewer leading to live births. This narrative review explores the potential of artificial intelligence (AI), machine learning (ML), and deep learning (DL) to enhance various stages of the IVF process. Personalization of ovarian stimulation protocols, gamete selection, and embryo annotation and selection are critical areas where AI may benefit significantly. AI-driven tools can analyze vast datasets to predict optimal stimulation protocols, potentially improving oocyte quality and fertilization rates. In sperm and oocyte quality assessment, AI can offer precise, objective analyses, reducing subjectivity and standardizing evaluations. In embryo selection, AI can analyze time-lapse imaging and morphological data to support the prediction of embryo viability, potentially aiding implantation outcomes. However, the role of AI in improving clinical outcomes remains to be confirmed by large-scale, well-designed clinical trials. Additionally, AI has the potential to enhance quality control and workflow optimization within IVF laboratories by continuously monitoring key performance indicators (KPIs) and facilitating efficient resource utilization. Ethical considerations, including data privacy, algorithmic bias, and fairness, are paramount for the responsible implementation of AI in IVF. Future research should prioritize validating AI tools in diverse clinical settings, ensuring their applicability and reliability. Collaboration among AI experts, clinicians, and embryologists is essential to drive innovation and improve outcomes in assisted reproduction. AI's integration into IVF holds promise for advancing patient care, but its clinical potential requires careful evaluation and ongoing refinement
Space Junk
No full textThis entry discusses the jettisoned materials produced in the course of human exploration and commercialisation of space, in earth orbit and beyond. Often described as space ‘junk’ or debris, such objects are produced in various ways and are rapidly increasing in number. The implications of such accumulation are discussed as well as proposals for mitigation and the prospects for international agreements. Speculative ventures in pursuit of the commercial realisation of space tourism and planetary colonisation are increasing and likely to lead to further orbital pollution
Evaluating the Wellbeing of ENT Trainees in the UK: Survey Findings
No full textObjectives
The Association of Otolaryngologists in Training wanted to assess trainee wellbeing.
Methods
A survey was developed, incorporating the Copenhagen Burnout Inventory, short Warwick–Edinburgh Mental Wellbeing Scale and Brief Resilience Scale plus questions on working conditions.
Results
There were 190 responses and while most respondents had low or moderate levels of burnout, 15% had high personal burnout and 13% had high work-related burnout. The mean wellbeing score for respondents was lower than the whole population mean. 39% reported mental wellbeing has been slightly affected in a negative way by their working environment and conditions in the last six months, and 26% reported it being significantly affected negatively. Of these, 43 respondents reported an impact on patient safety.
Conclusions
This first ever survey of ENT trainees in the UK identified several areas of concern including how the working environment and conditions affect trainee wellbeing and impact on patient safety