Diagnostic framework to validate clinical machine learning models locally on temporally stamped data by Maximilian Schuessler, Scott Fleming, Shannon Meyer, Tina Seto, Tina Hernandez-Boussard

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Physics-informed deep learning model for line-integral diagnostics across fusion devices by Cong Wang, Weizhe Yang, Haiping Wang, Renjie Yang, Jing Li, Zhijun Wang, Yixiong Wei, Xianli Huang, Chenshu Hu, Zhaoyang Liu, Xinyao Yu, Changqing Zou, Zhifeng Zhao

“…The incorporation of the PILF has been shown to correct the model’s predictions, bringing the back-projections closer to the actual inputs and reducing the errors associated with inversion algorithms. Besides, we have developed a synthetic data model to generate customized line-integral diagnostic datasets and have also collected soft x-ray diagnostic datasets from EAST and HL-2A. …”
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Prospective Validation and Usability Evaluation of a Mobile Diagnostic App for Obstructive Sleep Apnea by Pedro Amorim, Daniela Ferreira-Santos, Marta Drummond, Pedro Pereira Rodrigues

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Key gene screening and diagnostic model establishment for acute type a aortic dissection by Yue Pan, Zhiming Yu, Xiaoyu Qian, Xuesong Zhang, Qun Xue, Weizhang Xiao

“…Recent studies highlight the role of immune dysregulation, vascular smooth muscle cell (VSMC) apoptosis, and metabolic-epigenetic interactions in AD pathogenesis, underscoring the need for novel biomarkers and therapeutic targets.ObjectiveThis study aims to identify critical genes and molecular pathways associated with ATAAD, develop a multi-omics diagnostic model, and evaluate potential therapeutic interventions to improve clinical outcomes.MethodsTranscriptome datasets from the Gene Expression Omnibus (GEO) database were analyzed using differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms (SVM, Random Forest, LASSO regression). …”
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Diagnostic accuracy of artificial intelligence for obstructive sleep apnea detection: a systematic review by Sara Haghighat, Muhammed Joghatayi, Julien Issa, Sarina Azimian, Janet Brinz, Ali Ashkan, Akhilanand Chaurasia, Zahra Rahimian, Linda Sangalli

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Novel Insights into Non-Invasive Diagnostic Techniques for Cardiac Amyloidosis: A Critical Review by Marco Maria Dicorato, Paolo Basile, Giuseppe Muscogiuri, Maria Cristina Carella, Maria Ludovica Naccarati, Ilaria Dentamaro, Marco Guglielmo, Andrea Baggiano, Saima Mushtaq, Laura Fusini, Gianluca Pontone, Cinzia Forleo, Marco Matteo Ciccone, Andrea Igoren Guaricci

“…Finally, machine learning and artificial intelligence-derived algorithms are gaining ground in this scenario and provide the basis for future research. …”
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Diagnostic models for sepsis-associated encephalopathy: a comprehensive systematic review and meta-analysis by Tengfei Zhou, Tengfei Zhou, Xinming Tian, Wei Wang, Zhe Chu

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Evaluation of quantum contouring algorithms for treatment planning on MR abdominal images by Rachel Glenn, Tucker Netherton, Adrian Celaya, Adrian Celaya, Mohamed Eltaher, Beatrice Riviere, Eugene J. Koay, Eugene J. Koay, David Fuentes, David Fuentes

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ALGORITHM OF DIAGNOSIS AND TREATMENT OF PATIENTS WITH PELVIC TRAUMA COMPLICATED WITH RETROPERITONEAL HEMORRHAGE by A. M. Fayn, A. N. Smolyar, P. A. Ivanov, N. N. Zadneprovskiy

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A suggested algorithm for detection of multi drug-resistant tuberculosis in Zimbabwe by Beauty Makamure, Salome Makumbirofa, Tsitsi Bandason, Paul Leccese, Reggie Mutetwa, Val Robertson, Peter Mason

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Comparative Analysis of Computed Tomography and Laparoscopy Data in Preoperative Staging of Gastric Cancer by N. K. Silanteva, T. A. Аgababyan, A. A. Kholeva, V. Yu. Skoropad, Z. N. Shavladze, S. A. Ivanov, L. P. Zhavoronkov

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An Integrated Algorithm with Feature Selection, Data Augmentation, and XGBoost for Ovarian Cancer by Jingxun Cai, Zne-Jung Lee, Zhihxian Lin, Chih-Hung Hsu, Yun Lin

“…Finally, the XGBoost classifier is applied to classify the augmented data, achieving efficient predictions for ovarian cancer. These research findings strongly demonstrate that the diagnostic method proposed in this paper has a significant advantage in the predictive diagnosis of ovarian cancer, with an accuracy of 99.01% that surpasses the current technologies in use. …”
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A Systematic Review and Evaluation of Sustainable AI Algorithms and Techniques in Healthcare by Yehia Ibrahim Alzoubi, Ahmet E. Topcu, Ersin Elbasi

“…This systematic review paper categorizes and classifies AI algorithms and tools in the healthcare sector to support more sustainable practices, focusing on reducing energy use while maintaining high standards in diagnostic accuracy and patient outcomes. …”
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Algorithm for Computationally Efficient Imaging of Sound Speed in Conventional Ultrasound Sonography by Piotr KARWAT

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Random Oversampling-Based Diabetes Classification via Machine Learning Algorithms by G. R. Ashisha, X. Anitha Mary, E. Grace Mary Kanaga, J. Andrew, R. Jennifer Eunice

“…In this work, we propose an e-diagnostic model for diabetes classification via a machine learning algorithm that can be executed on the Internet of Medical Things (IoMT). …”
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Development and validation of multimodal deep learning algorithms for detecting pulmonary hypertension by Wei Zhao, Zhihua Huang, Xiaolin Diao, Zhan Yang, Zhihui Zhao, Yun Xia, Qing Zhao, Zhaohong Sun, Qunying Xi, Yanni Huo, Ou Xu, Jiahui Geng, Xin Li, Anqi Duan, Sicheng Zhang, Luyang Gao, Yijia Wang, Sicong Li, Qin Luo, Zhihong Liu

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Formation of the pediatric electroretinogram database parameters for the development of doctor’s decisionmaking algorithm by A. E. Zhdanov, A. Yu. Dolganov, V. N. Kazaykin, V. I. Borisov, V. O. Ponomarev, L. G. Dorosinsky, A. V. Lizunov, E. Luchian, X. Bao

“…Electroretinography become fundamental ophthalmological research method that may assesses the state of the retina. …”
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Genomic and algorithm-based predictive risk assessment models for benzene exposure by Minyun Jiang, Minyun Jiang, Na Cai, Na Cai, Juan Hu, Lei Han, Lei Han, Fanwei Xu, Baoli Zhu, Baoli Zhu, Baoli Zhu, Baoli Zhu, Boshen Wang

“…AimIn this research, we leveraged bioinformatics and machine learning to pinpoint key risk genes associated with occupational benzene exposure and to construct genomic and algorithm-based predictive risk assessment models.Subject and methodsWe sourced GSE9569 and GSE21862 microarray data from the Gene Expression Omnibus. …”
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Comparing approaches to ordering peanut component–resolved diagnostics to reduce the need for oral food challenges by Raymond Mak, MD, Edmond S. Chan, MD, Michael Irvine, PhD, Jia Yi Huang, MSC, James Hethey, MD, Sheila Hartstein, MD, Li Wang, MD

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Machine learning-based Diagnostic model for determining the etiology of pleural effusion using Age, ADA and LDH by Qing-Yu Chen, Shu-Min Yin, Ming-Ming Shao, Feng-Shuang Yi, Huan-Zhong Shi

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