Fetal electrocardiography and artificial intelligence for prenatal detection of congenital heart disease by Ivar R. deVries, Judith O. E. H. vanLaar, Marieke B. van der Hout‐van der Jagt, Sally‐Ann B. Clur, Rik Vullings

“…More research is required to improve performance and determine the benefits to clinical practice.…”
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Predicting Affinity Through Homology (PATH): Interpretable binding affinity prediction with persistent homology. by Yuxi Long, Bruce R Donald

“…Compared to current binding affinity prediction algorithms, PATH+ shows similar or better accuracy and is more generalizable across orthogonal datasets. …”
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Integrating status-neutral and targeted HIV testing in Zimbabwe: A complementary strategy. by Hamufare D Mugauri, Owen Mugurungi, Joconiah Chirenda, Kudakwashe Takarinda, Prosper Mangwiro, Mufuta Tshimanga

“…First tests were 65% more likely to test HIV positive (a95%CI: 1.43, 1.91) whilst screened patients were 3.89 times more likely to link to HIV prevention services (a95%CI: 3.05, 4.97), against 25.5% (n = 1,871) linkage among patients not screened.…”
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Effectiveness of mindfulness-based therapy, stress reduction in hypertension and prehypertension: a systematic review by D. I. Nozdrachev, M. N. Solovieva, K. A. Zamyatin

“…The systematic review was prepared according to the PRISMA algorithm with minor modifications. The search algorithm included articles in Russian and English, indexed in the Pubmed/MEDLINE and Cochrane Library databases. …”
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Diabetes and Cataracts Development—Characteristics, Subtypes and Predictive Modeling Using Machine Learning in Romanian Patients: A Cross-Sectional Study by Adriana Ivanescu, Simona Popescu, Adina Braha, Bogdan Timar, Teodora Sorescu, Sandra Lazar, Romulus Timar, Laura Gaita

“…<i>Conclusions:</i> These findings suggest that diabetes may impact the type of cataract that develops, with CC being notably more prevalent in diabetic patients. This has important implications for screening and management strategies for cataract formation in diabetic populations.…”
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Implementation costs and cost-effectiveness of ultraportable chest X-ray with artificial intelligence in active case finding for tuberculosis in Nigeria. by Tushar Garg, Stephen John, Suraj Abdulkarim, Adamu D Ahmed, Beatrice Kirubi, Md Toufiq Rahman, Emperor Ubochioma, Jacob Creswell

“…We provide implementation cost and cost-effectiveness estimates of different screening algorithms using symptoms, CXR and AI in Nigeria. …”
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Chinese AI tool ERNIE Bot Textual Exploration of False Information by Fu Yue

“…These countermeasures can not only enhance the detection ability of AI, but also give full play to human judgement in information screening, forming a more effective disinformation prevention mechanism. …”
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Optimization of a Coupled Neuron Model Based on Deep Reinforcement Learning and Application of the Model in Bearing Fault Diagnosis by Shan Wang, Jiaxiang Li, Xinsheng Xu, Ruiqi Wu, Yuhang Qiu, Xuwen Chen, Zijian Qiao

“…Using the SNR as the evaluation metric, the algorithm performs data screening on the replay buffer parameters before training the deep network for predicting coupled neuron model performance. …”
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Iterative phase contrast CT reconstruction with novel tomographic operator and data-driven prior. by Stefano van Gogh, Subhadip Mukherjee, Jinqiu Xu, Zhentian Wang, Michał Rawlik, Zsuzsanna Varga, Rima Alaifari, Carola-Bibiane Schönlieb, Marco Stampanoni

“…Moreover, the highly ill-conditioned differential nature of the GI-CT forward operator renders the inversion from corrupted data even more cumbersome. In this paper, we propose a novel regularized iterative reconstruction algorithm with an improved tomographic operator and a powerful data-driven regularizer to tackle this challenging inverse problem. …”
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Analysis of Facial Areas to Identify CHD Risks Based on Facial Textures by Budi Sunarko, Agung Adi Firdaus, Yudha Andriano Rismawan, Anan Nugroho

“…Early screening for coronary heart disease (CHD) remains insufficiently addressed, underscoring the need for a more effective screening tool. …”
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Diagnosing cystic fibrosis in low- and middle-income countries: challenges and strategies by Michèle Fuhrer, Marco Zampoli, Hugues Abriel

“…Recent evidence indicates that Cystic Fibrosis is more common than initially thought and is likely underreported in low- and middle-income countries. …”
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Assessment of salt tolerance in peas using machine learning and multi-sensor data by Zehao Liu, Qiyan Jiang, Yishan Ji, Rong Liu, Hongquan Liu, Xiuxiu Ya, Zhenxing Liu, Zhirui Wang, Xiuliang Jin, Tao Yang

“…Recent advancements in Unmanned aerial vehicle (UAV) and sensor technologies have enabled high-throughput screening of salt-tolerant crops, offering a more efficient alternative. …”
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AI-Powered Synthesis of Structured Multimodal Breast Ultrasound Reports Integrating Radiologist Annotations and Deep Learning Analysis by Khadija Azhar, Byoung-Dai Lee, Shi Sub Byon, Kyu Ran Cho, Sung Eun Song

“…Our method synthesizes comprehensive breast US reports by combining the extracted information from radiologists’ annotations during routine screenings with the analysis results from deep learning algorithms on multimodal US images. …”
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Evaluation of three commercial rapid immunoassays for the diagnosis of Clostridioides difficile infection by Hannes Bjarki Vigfússon, Theresa Ennefors, Torbjörn Norén, Martin Sundqvist

“…The C. diff Quik Chek Complete performed the best of the three immunoassays, and when used in combination with NAAT, is a viable option for the laboratory diagnosis of CDI.IMPORTANCELaboratory diagnosis of Clostridioides difficile infection is complex, and current guidelines recommend a two-step diagnostic algorithm with a sensitive screening test and a more specific confirmatory test. …”
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Breast mass lesion area detection method based on an improved YOLOv8 model by Yihua Lan, Yingjie Lv, Jiashu Xu, Yingqi Zhang, Yanhong Zhang

“…These improvements provide a more efficient and accurate tool for clinical breast cancer screening and lay the foundation for subsequent studies. …”
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A Combined Deep CNN: LSTM with a Random Forest Approach for Breast Cancer Diagnosis by Almas Begum, V. Dhilip Kumar, Junaid Asghar, D. Hemalatha, G. Arulkumaran

“…Computer-aided diagnosis (CAD) has minimum intervention of humans and produces more accurate results than humans. It will be a difficult and long task that depends on the expertise of pathologists. …”
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Principles of effective out-patient diagnostics of diffuse liver diseases by Komova A. G., M. V. Mayevskaya, V. T. Ivashkin

“…Abnormal liver functional tests were revealed in 30,6% of industrial city inhabitants — in 1461 of 4768 cases (232 patients were excluded from the study due to partial data loss), significantly more frequently in men in comparison to women, i.e. 49,7 and 25,5% respectively (p&lt;0,001). …”
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Development of a high-performing, cost-effective and inclusive Afrocentric predictive model for stroke: a meta-analysis approach  by M Nweke, P Oyirinnaya, P Nwoha, SB Mitha, N Mshunqane, N Govender, M Ukwuoma, SC Ibeneme

“…Results More than 50% of the eligible studies examined both ischemic and hemorrhagic stroke. …”
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Increasing comprehensiveness and reducing workload in a systematic review of complex interventions using automated machine learning by Olalekan A Uthman, Rachel Court, Jodie Enderby, Lena Al-Khudairy, Chidozie Nduka, Hema Mistry, GJ Melendez-Torres, Sian Taylor-Phillips, Aileen Clarke

“…Background As part of our ongoing systematic review of complex interventions for the primary prevention of cardiovascular diseases, we have developed and evaluated automated machine-learning classifiers for title and abstract screening. The aim was to develop a high-performing algorithm comparable to human screening. …”
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Comparison between Logistic Regression and K-Nearest Neighbour Techniques with Application on Thalassemia Patients in Mosul by Mohammed Al jbory, Hutheyfa Taha

“…The data was divided into 70% for training and 30% for screening.&nbsp;The experimental results showed that the logistic regression model performed better than the nearest neighbor algorithm with a precision of 96%, recall of 98%, and F1- score of 97% in the thalassemia intermedia category, while it had a precision of 97%, recall of 95%, and F1- score of 96% in the thalassemia major category, indicating that logistic regression performed well in distinguishing between these two categories. it has been shown that logistic regression is more effective than the K-nearest neighbor algorithm in classifying thalassemia patients, especially those with thalassemia major. …”
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