Developing a Transparent Anaemia Prediction Model Empowered With Explainable Artificial Intelligence by Muhammad Sajid Farooq, Muhammad Hassan Ghulam Muhammad, Oualid Ali, Zahid Zeeshan, Muhammad Saleem, Munir Ahmad, Sagheer Abbas, Muhammad Adnan Khan, Taher M. Ghazal

“…The worldwide health epidemic of anaemia which is a condition with low levels of red blood cells or haemoglobin requires accurate prediction models to act promptly and improve patient outcomes because it is widespread and has different causes. The effective management of anaemia is piled with obstructions, which may include the variability of diagnostic criteria, the resource limitations of healthcare, and the multifactorial nature of the disease including nutritional deficiencies, chronic disease, and genetic factors. …”
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Unraveling the oxidative stress landscape in diabetic foot ulcers: insights from bulk RNA and single-cell RNA sequencing data by Jialiang Lin, Linjuan Huang, Weiming Li, Haijun Xiao, Mingmang Pan

“…The expression patterns of BCL2 and 和FOXP2 across the different groups were consistent with findings from bulk RNA sequencing analysis. …”
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Optimizing radiomics for prostate cancer diagnosis: feature selection strategies, machine learning classifiers, and MRI sequences by Eugenia Mylona, Dimitrios I. Zaridis, Charalampos Ν. Kalantzopoulos, Nikolaos S. Tachos, Daniele Regge, Nikolaos Papanikolaou, Manolis Tsiknakis, Kostas Marias, ProCAncer-I Consortium, Dimitrios I. Fotiadis

“…Critical relevance statement This work may guide future radiomic research, paving the way for the development of more effective and reliable radiomic models; not only for advancing prostate cancer diagnostic strategies, but also for informing broader applications of radiomics in different medical contexts. …”
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Identification of progression-related genes and construction of prognostic model for chronic kidney disease by machine learning by Bingkun Zhou, Hu Zhou, Xiaodong Huang, Shijie Liu

“…However, there is currently a lack of CKD prognostic prediction models based on transcriptomics and machine learning.MethodsUtilizing weighted correlation network analysis (WGCNA) and random forest algorithms in GSE137570, three core gene sets of different sizes were constructed, which were externally validated in GSE66494 and GSE180394, and evaluated for their predictive performance in GSE45980 by receiver operating characteristic (ROC) curves. …”
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Critical load exceedances for North America and Europe using an ensemble of models and an investigation of causes of environmental impact estimate variability: an AQMEII4 study by P. A. Makar, P. Cheung, C. Hogrefe, A. Akingunola, U. Alyuz, J. O. Bash, M. D. Bell, R. Bellasio, R. Bianconi, T. Butler, H. Cathcart, O. E. Clifton, O. E. Clifton, A. Hodzic, I. Kioutsioukis, R. Kranenburg, A. Lupascu, A. Lupascu, J. A. Lynch, K. Momoh, J. L. Perez-Camanyo, J. Pleim, Y.-H. Ryu, R. San Jose, D. Schwede, D. Schwede, T. Scheuschner, M. W. Shephard, R. S. Sokhi, S. Galmarini

“…The reasons for this variation were examined in detail by first ranking the relative contribution of different sources of sulfur and nitrogen deposition in terms of deposited mass and model-to-model variability in that deposited mass, followed by their analysis using AQMEII4 diagnostics, along with evaluation of the most recent literature.…”
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Identification of osteoarthritis-associated chondrocyte subpopulations and key gene-regulating drugs based on multi-omics analysis by Ting Hao, Zhiwei Pei, Sile Hu, Zhenqun Zhao, Wanxiong He, Jing Wang, Liuchang Jiang, jirigala Ariben, Lina Wu, Xiaolong Yang, Leipeng Wang, Yonggang Wu, Xiaofeng Chen, Qiang Li, Haobo Yang, Siqin Li, Xing Wang, Mingqi Sun, Baoxin Zhang

“…To screen for hub genes associated with OA, a combination of 10 machine learning algorithms and 113 algorithm compositions was integrated. …”
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Scalable Clustering of Complex ECG Health Data: Big Data Clustering Analysis with UMAP and HDBSCAN by Vladislav Kaverinskiy, Illya Chaikovsky, Anton Mnevets, Tatiana Ryzhenko, Mykhailo Bocharov, Kyrylo Malakhov

“…The study aims to apply unsupervised clustering algorithms to ECG data to detect latent risk profiles related to heart failure, based on distinctive ECG features. …”
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Identifying potential three key targets gene for septic shock in children using bioinformatics and machine learning methods by Wei Guo, Hao Chen, Feng Wang, Yingjiao Chi, Wei Zhang, Shan Wang, Kezhu Chen, Hong Chen

“…Three kinds of machine learning models were established, and the candidate genes were screened by intersection to obtain the core genes with diagnostic value. ROC curve was drawn for core genes to clarify the diagnostic value of genetic markers.ResultsAnalysis of differences in the preprocessed dataset identified 83 genes, including 78 up-regulated genes and 5 down-regulated genes. 17 candidate genes were screened by protein interaction network analysis. …”
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Incidence and prevalence of idiopathic pulmonary fibrosis: a systematic literature review and meta-analysis by Negar Golchin, Aditya Patel, Julia Scheuring, Victoria Wan, Kimberly Hofer, Jean-Paul Collet, Brandon Elpers, Tamara Lesperance

“…Additional contributing factors include variations in case identification algorithms, differences in diagnostic definitions and regional differences in occupational and environmental exposures. …”
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Multimodal marvels of deep learning in medical diagnosis using image, speech, and text: A comprehensive review of COVID-19 detection by Md Shofiqul Islam, Khondokar Fida Hasan, Hasibul Hossain Shajeeb, Humayan Kabir Rana, Md. Saifur Rahman, Md. Munirul Hasan, AKM Azad, Ibrahim Abdullah, Mohammad Ali Moni

“…We explore the architecture of deep learning models, emphasising their data-specific structures and underlying algorithms. Subsequently, we compare different deep learning strategies utilised in COVID-19 analysis, evaluating them based on methodology, data, performance, and prerequisites for future research. …”
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A nomogram for predicting the nature of thyroid adenomatoid nodules on ultrasound: a dual-center study by Sheng Cheng, Xian-Tao Zeng, Xia Liang, Zhi-Liang Hong, Jian-Chuan Yang, Zi-Ling You, Song-Song Wu

“…Firstly, radiomics nomograms (R_Nomogram) and clinical nomograms (C_Nomogram) were constructed using eight machine-learning algorithms. The best R_Nomogram and C_Nomogram generated the Radiomics-clinical nomogram (R-C_nomogram). …”
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Exploring the Potential Regulatory Mechanisms of Mitophagy in Ischemic Cardiomyopathy by Li Z, Kong J, Xi S, Jin Z, Yang F, Zhu Z, Liu L

“…The four biomarkers (PPDPF, DPEP2, LTBP1, SOCS2) were acquired, and all biomarkers had good diagnostic efficacy for ICM. The content of 3 immune cells between ICM and control groups was significantly different, namely T cells, CD8+ T cells, and neutrophil, and all biomarkers were considerably positively correlated with T cells. …”
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Combination of ultrasound-based radiomics and deep learning with clinical data to predict response in breast cancer patients treated with neoadjuvant chemotherapy by Wu Tenghui, Liu Xinyi, Si Ziyi, Zhang Yanting, Ma Ziqian, Zhu Yiwen, Gan Ling

“…Multiple machine learning algorithms were employed to model and validate the diagnostic performance of different types of features. …”
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A toolbox for the identification of foot-floor contact sequences to analyze atypical gait cycles in a real-life scenario: application on patients after proximal femur fracture and... by Marco Ghislieri, Nicolas Leo, Marco Caruso, Clemens Becker, Andrea Cereatti, Valentina Agostini

“…Abstract Background The detection of gait subphases is pivotal for a comprehensive assessment of gait quality, playing a key role in different applications such as rehabilitation programs, movement disorder diagnostics, and fall prevention strategies. …”
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AI-based thematic exploration to understand patients with myasthenia gravis by serological subtype by Louis Jackson, Caroline Brethenoux, Alyssa DeLuca, Jacqueline Pesa, Zia Choudhry, Patrick Furey, Rosario Alvarez, Laura Gonzalez, Alex Lorenzo, Raghav Govindarajan, Ashley E. L. Anderson

“…ObjectiveMyasthenia gravis (MG) is challenging to diagnose and appropriate treatment is informed by serological versus diagnostic testing. Digital conversations can reveal insights into patient perceptions and concerns that may differ across autoantibody subtypes. …”
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Interleukin-4 and specific IgE to oranges levels study in persons with allergic anamnesis by A. S. Prilutskiy, N. B. Abylgazinova, K. Y. Tkachenko

“…The data obtained from the study might be useful both in public health and in further research in order to improve diagnostic and treatment algorithms in orange sensitized patients.…”
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The role of artificial intelligence in promoting health and developing preventive strategies for diabetes by Ameneh Marzban

“…Moreover, challenges such as algorithmic bias, generalizability across diverse populations, and the necessity for clinician training must be carefully considered. …”
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The identification and validation of histone acetylation-related biomarkers in depression disorder based on bioinformatics and machine learning approaches by Lu Zhang, Lu Zhang, YuJing Lv, Mengqing Ma, Jile Lv, Jie Chen, Shang Lei, Yi Man, Guimei Xing, Yu Wang

“…Two machine learning algorithms were used to identify hub genes, which were used for drug prediction, immunological infiltration studies, nomogram construction, and regulatory network building. …”
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The incremental value of tuberculosis detecting African giant pouched rats over Smear microscopy and Xpert MTB/RIF for Tanzanian TB detection. by Tefera B Agizew, Joseph Soka, Stephen Mwimanzi, Cynthia D Fast, Gilbert Mwesiga, Nashon Edward, Marygiven Stephen, Rehema Kondo, Christophe Cox, Negussie Beyene

“…Sputum-smear microscopy (smear) is being replaced by Xpert MTB/RIF (Xpert)-based diagnostic algorithms in many countries. We evaluated the incremental values of rat-based case detection over smear and Xpert.…”
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Identification of biomarkers and immune microenvironment associated with pterygium through bioinformatics and machine learning by Li-Wei Zhang, Ji Yang, Hua-Wei Jiang, Hua-Wei Jiang, Xiu-Qiang Yang, Ya-Nan Chen, Wei-Dang Ying, Ying-Liang Deng, Min-hui Zhang, Hai Liu, Hong-Lei Zhang

“…The diagnostic potential of these genes was validated using external datasets (GSE2513 and GSE51995). …”
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