Automatic identification of clinically important Aspergillus species by artificial intelligence-based image recognition: proof-of-concept study by Chi-Ching Tsang, Chenyang Zhao, Yueh Liu, Ken P. K. Lin, James Y. M. Tang, Kar-On Cheng, Franklin W. N. Chow, Weiming Yao, Ka-Fai Chan, Sharon N. L. Poon, Kelly Y. C. Wong, Lianyi Zhou, Oscar T. N. Mak, Jeremy C. Y. Lee, Suhui Zhao, Antonio H. Y. Ngan, Alan K. L. Wu, Kitty S. C. Fung, Tak-Lun Que, Jade L. L. Teng, Dirk Schnieders, Siu-Ming Yiu, Susanna K. P. Lau, Patrick C. Y. Woo

“…Results demonstrated that ResNet-18 outperformed Inception-v3 and DenseNet-121 and is the best algorithm of choice because it made the fewest misidentifications (n = 8) and possessed the highest testing accuracy (99.35%). …”
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Comparative assessment of line probe assays and targeted next-generation sequencing in drug-resistant tuberculosis diagnosisResearch in context by Giovanna Carpi, Marva Seifert, Andres De la Rossa, Swapna Uplekar, Camilla Rodrigues, Nestani Tukvadze, Shaheed V. Omar, Anita Suresh, Timothy C. Rodwell, Rebecca E. Colman

“…Interpretation: LPAs demonstrated lower sensitivity and more limited drug resistance detection compared to tNGS workflows, underscoring the advantages of tNGS for improving DR-TB diagnostic algorithms. These findings provide critical evidence to guide updates in DR-TB diagnostic programs. …”
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Breast cancer diagnosis with MFF-HistoNet: a multi-modal feature fusion network integrating CNNs and quantum tensor networks by Tariq Mahmood, Tanzila Saba, Amjad Rehman

“…Computer-aided diagnosis (CAD) technology can improve efficiency, accuracy, and treatment options. The existing algorithms for classifying breast cancer histopathological images have limitations, including high parameter counts, ineffective extraction of global features, and substantial time costs, which result in the loss of valuable information. …”
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Application of machine learning for the analysis of peripheral blood biomarkers in oral mucosal diseases: a cross-sectional study by Huiyu Yao, Zixin Cao, Liangfu Huang, Haojie Pan, Xiaomin Xu, Fucai Sun, Xi Ding, Wan Wu

“…The Random Forest model demonstrated high accuracy (94.68%) in classifying disease groups, emphasizing the potential of machine learning to enhance diagnostic precision in oral mucosal diseases. Future research should focus on validating these findings in larger cohorts and exploring alternative machine-learning algorithms to improve diagnostic accuracy further.…”
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Biomedical Data Analysis Systems for the Diagnosis of Skin Neoplasms by O. O. Myakinin

“…Materials and methods. Algorithms of software modules developed for optical systems of our own design are presented. …”
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Lateral shear interferometry for shape accuracy measurements of 3D-printed micro-optics by Zhao Yanqiu, Wang Lunwei, Siegle Leander, Giessen Harald

“…This unprecedented level of accessibility and adaptability holds immense promise for diagnostic and therapeutic interventions in the realm of cardiovascular medicine and beyond. …”
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A stacked learning framework for accurate classification of polycystic ovary syndrome with advanced data balancing and feature selection techniques by Heba M. Emara, Walid El-Shafai, Naglaa F. Soliman, Abeer D. Algarni, Reem Alkanhel, Fathi E. Abd El-Samie

“…Moreover, beyond immediate applications, this paper explores the generalization of the proposed framework, demonstrating its adaptability and efficacy across different medical classifications. This versatility is exemplified by its successful application to cervical cancer, showcasing the framework potential as a pioneering force in reshaping the landscape of machine-learning applications in healthcare diagnostics.…”
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3D Jones-matrix thesiography of biological fluid facies by Alexander Ushenko, Iryna Soltys, Alexander Dubolazov, Yuriy Ushenko, Viacheslav Bilookyi, Olexander Bilookyi, Olexandra Litvinenko, Ivan Mikirin, Jun Zheng, Zhebo Chen, Lin Bin

“…A new polarization–interference biomedical diagnostic three-dimensional (3D) Jones-matrix technology with digital Fourier reconstruction of layered maps of optical anisotropy (thesiograms) of dehydrated films (facies) of biological fluids of human organs is presented and experimentally tested. …”
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Boerhaave Syndrome - A Rare but Serious Esophageal Emergency by Paula Szarek, Jakub Kołacz, Patrycja Oleś, Marcin Kwiatkowski, Paulina Bochniak

“…Aim of the Study: This review aims to provide a comprehensive overview of the syndrome’s definition, pathophysiology, clinical characteristics, diagnostic algorithms, and current therapeutic strategies, emphasizing the pivotal role of early intervention in reducing morbidity and mortality. …”
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Association between the development of sepsis and the triglyceride-glucose index in acute pancreatitis patients: a retrospective investigation utilizing the MIMIC-IV database by Lin Xu, Xuejing Li, Na Zhang, Chunmei Guo, Pan Wang, Min Gao, Yanhui Zhang, Lixin Zhao

“…Utilizing the formula ln[(triglycerides mg/dl) × (glucose mg/dl)/2], the TyG index was calculated. The Boruta algorithm and Xgboost model were used for feature selection in order to pinpoint the important variables affecting results. …”
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Preliminary exploratory study on differential diagnosis between benign and malignant peripheral lung tumors: based on deep learning networks by Yuan Wang, Yuan Wang, Yutong Zhang, Yongxin Li, Tianyu She, Meiqing He, Hailing He, Dong Zhang, Dong Zhang, Jue Jiang

“…DeLong’s test was employed to compare the differences between the groups.ResultsAmong the five models, the one based on the ResNet18 algorithm demonstrated the highest performance. …”
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Deep learning-based time series prediction in multispectral and hyperspectral imaging for cancer detection by Lijun Hao, Changmin Wang, Jinshan Che, Mingming Sun, Yuhong Wang

“…The model employs self-supervised learning to mitigate the scarcity of labeled medical data, enhancing generalization across different imaging modalities. Furthermore, a knowledge-guided regularization module is introduced to leverage prior medical knowledge, refining predictions and reducing false positives.ResultsExperimental results demonstrate that our framework outperforms state-of-the-art methods in spectral imaging-based cancer detection, achieving superior accuracy, robustness, and interpretability.DiscussionThe proposed approach provides a significant step toward AI-driven medical imaging solutions that effectively harness multispectral and hyperspectral data for enhanced diagnostic performance.…”
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Toward a fair, gender-debiased classifier for the diagnosis of attention deficit/hyperactivity disorder- a Machine-Learning based classification study by Susanne Neufang, Feifei Li, Atae Akhrif, Oya D. Beyan

“…The delayed diagnosis in females has been attributed to an inequality in the diagnostic criteria, failing to focus on the gender differences regarding symptomatology, comorbidity, and societal factors contributing to this disparity. …”
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Protocol and research program of the European registry and biobank for interstitial lung diseases (eurILDreg) by Ekaterina Krauss, Silke Tello, Jennifer Naumann, Sandra Wobisch, Clemens Ruppert, Stefan Kuhn, Poornima Mahavadi, Raphael W. Majeed, Philippe Bonniaud, Maria Molina-Molina, Athol Wells, Nik Hirani, Carlo Vancheri, Simon Walsh, Matthias Griese, Bruno Crestani, Andreas Guenther, on behalf of further eurILDreg investigators, RARE-ILD investigators

“…Drawn against this background, experts across pediatric and adult ILD fields have joined forces in the RARE-ILD initiative to pioneer novel non-invasive diagnostic algorithms and biomarkers. Collaborating with the RARE-ILD consortium, the eurILDreg aims to comprehensively describe different ILDs, analyze genetically defined forms across age groups, create innovative diagnostic and therapeutic biomarkers, and employ artificial intelligence for data analysis. …”
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Narrowband shortwave minima in spectra of backscattered light from the sea obtained from ocean color scanners as a remote indication of algal blooms by G.S. Karabashev, M.A. Evdoshenko

“…Further studies are needed to convert the indices to band-difference algorithms for retrieving the bio-optical characteristics of algal blooms.…”
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Application of artificial intelligence in the diagnosis of malignant digestive tract tumors: focusing on opportunities and challenges in endoscopy and pathology by Yinhu Gao, Peizhen Wen, Yuan Liu, Yahuang Sun, Hui Qian, Xin Zhang, Huan Peng, Yanli Gao, Cuiyu Li, Zhangyuan Gu, Huajin Zeng, Zhijun Hong, Weijun Wang, Ronglin Yan, Zunqi Hu, Hongbing Fu

“…The effects and main limitations of AI diagnosis were summarized through comprehensive analysis of research design, algorithmic methods, and experimental results from relevant literature. …”
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USING ARTIFICIAL INTELLIGENCE IN OBSTETRICS TO DIAGNOSE FETAL MALFORMATIONS AND PREVENT DISEASES by Елена Валерьевна Литвинова, Оксана Владимировна Носкова

“…The effectiveness of using artificial intelligence and various algorithms based on it to improve the analysis of two-dimensional (2D) and three-dimensional (3D) ultrasound images of fetal structures, assessment of organ function and diagnosis of diseases was proved. …”
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THE DIAGNOSIS TECHNIQUE OF ABNORMAL HEATING OF POWER CAPACITORS by D. I. Zalizny

“…The existing system of protection and diagnostics are not able to detect abnormal heating of the power capacitors caused by its internal malfunction formation. …”
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Role of Aging in Ulcerative Colitis Pathogenesis: A Focus on ETS1 as a Promising Biomarker by Ni M, Peng W, Wang X, Li J

“…A series of machine learning algorithms was used to screen two feature genes (ETS1 and IL7R) to establish the diagnostic model, which exhibited satisfactory diagnostic efficiency. …”
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Unveiling psychobiological correlates in primary Sjögren’s syndrome: a machine learning approach to determinants of disease burden by László V. Módis, László V. Módis, András Matuz, András Matuz, Zsófia Aradi, Ildikó Fanny Horváth, Antónia Szántó, Antal Bugán

“…Three machine learning algorithms were trained to predict outcome variables, first by each measure category, then on the entire set of predictor variables. …”
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