Characteristic genes and immune landscape of interstitial cystitis. by Junhao Wang, Yan Zhou, Jie Hu, Jianpeng Han, Jianyong Feng, Kuo Guo, Wenbin Chen, Yanrui Yun, Yongzhang Li

“…<h4>Conclusion</h4>We screened the feature genes, CCL18, MMP10 and WIF1, among the differentially expressed genes (DEGs) by three different machine learning algorithms. They showed good diagnostic performance in both training and testing cohorts and were potential diagnostic markers for IC. …”
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Comparative Study of CNN Architectures for Brain Tumor Classification Using MRI: Exploring GradCAM for Visualizing CNN Focus by Areli Chinga, Wilden Bendezu, Antonio Angulo

“…This study aims to refine the diagnosis of brain tumors using convolutional neural network algorithms. Currently, diagnostic accuracy is limited, therefore, our approach uses five different CNN architectures to accurately identify and classify affected brain regions, specifically glioma, meningioma, or pituitary tumors. …”
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AI-driven healthcare: Fairness in AI healthcare: A survey. by Sribala Vidyadhari Chinta, Zichong Wang, Avash Palikhe, Xingyu Zhang, Ayesha Kashif, Monique Antoinette Smith, Jun Liu, Wenbin Zhang

“…However, these advancements also introduce substantial ethical and fairness challenges, particularly related to biases in data and algorithms. These biases can lead to disparities in healthcare delivery, affecting diagnostic accuracy and treatment outcomes across different demographic groups. …”
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Mixed reality infrastructure based on deep learning medical image segmentation and 3D visualization for bone tumors using DCU-Net by Kun Wang, Yong Han, Yuguang Ye, Yusi Chen, Daxin Zhu, Yifeng Huang, Ying Huang, Yijie Chen, Jianshe Shi, Bijiao Ding, Jianlong Huang

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Deep Learning for Ultrasonographic Assessment of Temporomandibular Joint Morphology by Julia Lasek, Karolina Nurzynska, Adam Piórkowski, Michał Strzelecki, Rafał Obuchowicz

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Advanced QT interval analysis in long-term electrocardiography using shape-based clustering and template matching: A novel approach for Holter monitoring by Kaoru Hatano, Tomohiro Takata, Mineki Takechi, Akane Wada, Hirohisa Taniguchi, Yuichi Tamura

“…For all waves, the mean and standard deviation of the difference between the algorithm and the expert analysis was 0.5 (±16), and that of the difference between the DSC5500 and the expert analysis was −16.4 (±42.8). …”
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HOW I TREAT PH+ ACUTE LYMPHOBLASTIC LEUKEMIA by Robin Foà

“…The possibility of offering such a personalized frontline management to all patients – including the elderly – strongly relies on adequate and standardized laboratory facilities aimed at a broad diagnostic work-up and at an accurate monitoring of MRD, as well as an optimal and timely access to the different drugs. …”
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Automated analysis of retinal microcirculation in type 1 diabetes mellitus by Yu. N. Yusef, M. H. Durzhinskaya, V. G. Pavlov, D. V. Petrachkov, I. B. Gurevich, V. V. Yashina, A. T. Tleubaev, V. V. Fadeyev, I. V. Poluboyarinova, A. E. Goldsmid, R. А. Karamullina, D. V. Lipatov, M. V. Budzinskaya

“…BACKGROUND: The paper is dedicated to the assessment of the retinal microvasculature in patients with type 1 diabetes mellitus (DM) with various features of the clinical course and different stages of diabetic retinopathy (DR). Automatic analysis of optical coherence tomogram angiograms (OCT-A) was carried out with specially developed software that provides the ability to estimate quantitative vascular parameters.AIM: The purpose of the study was to assess diagnostic accuracy of clinical parameters and imaging biomarkers in type 1 diabetes using a new algorithm for OCT-A analysis.MATERIALS AND METHODS: The study involved 186 people (365 eyes) with type 1 diabetes. …”
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Development and external validation of machine learning-based models to predict patients with cellulitis developing sepsis during hospitalisation by Li Hu, Xilingyuan Chen, Rentao Yu

“…This study was designed to develop and compare different models for predicting patients with cellulitis developing sepsis during hospitalisation.Design This is a retrospective cohort study.Setting This study included both the development and the external-validation phases from two independent large cohorts internationally.Participants and methods A total of 6695 patients with cellulitis in the Medical Information Mart for Intensive care (MIMIC)-IV database were used to develop models with different machine-learning algorithms. …”
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Helium retention feature in the boron deposited layer on tungsten substrate by laser-induced breakdown spectroscopy and machine learning approach by Muhammad Amir Shabbir, Ran Hai, Zhonglin He, Zehua Liu, Fahad Rehman, Xue Bai, Jianping Mu, Ding Wu, Cong Li, Hongbin Ding

“…Machine learning (ML) algorithms are used to classify the co-deposits and substrates. …”
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Distinguishing novel coronavirus influenza A virus pneumonia with CT radiomics and clinical features by Lianyu Sui, Huan Meng, Jianing Wang, Wei Yang, Lulu Yang, Xudan Chen, Liyong Zhuo, Lihong Xing, Yu Zhang, Jingjing Cui, Xiaoping Yin

“…Area under ROC curve (AUC) were calculated to evaluate the diagnostic efficiency, and Delong’s test was used to compare the AUC between different models. …”
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Randomization-Driven Hybrid Deep Learning for Diabetic Retinopathy Detection by A. M. Mutawa, G. R. Hemalakshmi, N. B. Prakash, M. Murugappan

“…This fusion of deep learning innovation with the principles of randomization-based algorithms opens new avenues for developing accessible, accurate diagnostic tools, potentially alleviating the global impact of diabetic vision loss.…”
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THE PERCENTAGE AND STRUCTURE OF PERODONT TISSUE DISEASES IN PATIENTS OF LVIV REGIONAL STATIONARY CLINICAL HOSPITAL DEPENDING ON AGE by M. М. Shevchuk

“…To study the prevalence and structure of periodontal tissue lesions in patients with general - somatic diseases in order to develop a therapeutic algorithm in a hospital setting. Materials and methods. …”
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Chondrogenic Cancer Grading by Combining Machine and Deep Learning with Raman Spectra of Histopathological Tissues by Gianmarco Lazzini, Mario D’Acunto

“…In particular, in the last years several studies have demonstrated how the diagnostic performances of RS can be significantly improved by employing machine learning (ML) algorithms for the interpretation of Raman-based data. …”
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A study on early diagnosis for fracture non-union prediction using deep learning and bone morphometric parameters by Hui Yu, Qiyue Mu, Zhi Wang, Yu Guo, Jing Zhao, Guangpu Wang, Qingsong Wang, Xianghong Meng, Xiaoman Dong, Shuo Wang, Jinglai Sun

“…Through ablation studies, comparative experiments, and result analysis, the algorithm's effectiveness and superiority were validated. …”
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Vascular anomalies in childhood. Review and update by Juan Carlos López Gutiérrez, Jesús Pozo Losada, Manuel Gómez Tellado

“…They are completely different diseases, although they are often grouped under the umbrella term of vascular anomalies.The diagnostic algorithm used in the clinical evaluation of vascular anomalies should be based on a thorough history-taking and detailed physical examination.Future knowledge in this field will be based above all on genetic findings and therapeutic innovations. …”
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Sound-Based Unsupervised Fault Diagnosis of Industrial Equipment Considering Environmental Noise by Jeong-Geun Lee, Kwang Sik Kim, Jang Hyun Lee

“…Moreover, applying DANN for fault diagnosis significantly improved diagnostic performance in noisy environments by overcoming environmental differences between the source and target domains. …”
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Comparative analysis of machine learning models for malaria detection using validated synthetic data: a cost-sensitive approach with clinical domain knowledge integration by Gudi V. Chandra Sekhar, Chekol Alemu

“…Abstract Malaria remains a critical global health challenge requiring innovative diagnostic approaches. Machine learning offers promising solutions for automated detection, but systematic algorithm comparison using clinically validated data remains limited. …”
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The effectiveness of a novel artificial intelligence (AI) model in detecting oral and dental diseases by Ravi Rathod, Saffa Dean, Christopher Sproat

“…AI integration has the scope to enhance clinicians’ diagnostic workflows in dentistry, with advancements in neural networks and machine learning poised to solidify its role as a valuable diagnostic aid.…”
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Screening of serum biomarkers in patients with PCOS through lipid omics and ensemble machine learning. by Ji-Ying Chen, Wu-Jie Chen, Zhi-Ying Zhu, Shi Xu, Li-Lan Huang, Wen-Qing Tan, Yong-Gang Zhang, Yan-Li Zhao

“…Results indicated that different lipid metabolites not only differ between groups but also have close effects on different corresponding clinical indexes. …”
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