Continuous heart rate measurements in patients with cardiac disease: Device comparison and development of a novel artefact removal procedure by Paulien Vermunicht, Katsiaryna Makayed, Christophe Buyck, Lieselotte Knaepen, Juan Sebastian Piedrahita Giraldo, Sebastiaan Naessens, Wendy Hens, Emeline Van Craenenbroeck, Kris Laukens, Lien Desteghe, Hein Heidbuchel

“…We developed and assessed an artefact removal procedure (ARP) using logistic regression machine learning models to detect unreliable PPG data. Results The ECG-based chest strap showed a strong correlation ( r  = 0.94) and clinically acceptable errors (mean absolute error, MAE = 3.4 bpm; mean absolute percentage error, MAPE = 4.9%). …”
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Artificial Intelligence–Enabled ECG Screening for LVSD in LBBB by Hak Seung Lee, MD, Sooyeon Lee, MD, Sora Kang, MS, Ga In Han, MS, Ah-Hyun Yoo, MS, Jong-Hwan Jang, PhD, Yong-Yeon Jo, PhD, Jeong Min Son, MD, Min Sung Lee, MD, MS, Joon-myoung Kwon, MD, MS, Kyung-Hee Kim, MD, PhD

“…The general model demonstrated high sensitivity, whereas the transfer learning model exhibited superior specificity. Conclusions: Our findings indicate that a broad AI-ECG model reliably detects LVSD in LBBB patients, and transfer learning offers modest improvements without requiring curated LBBB data sets. …”
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Metabolic pathway activation and immune microenvironment features in non-small cell lung cancer: insights from single-cell transcriptomics by Yanru Liu, Yanru Liu, Yanru Liu, Hanmin Liu, Hanmin Liu, Ying Xiong, Ying Xiong

“…IntroductionIn this study, we aim to provide a deep understanding of the tumor microenvironment (TME) and its metabolic characteristics in non-small cell lung cancer (NSCLC) through single-cell RNA sequencing (scRNAseq) data obtained from public databases. Given that lung cancer is a leading cause of cancer-related deaths globally and NSCLC accounts for the majority of lung cancer cases, understanding the relationship between TME and metabolic pathways in NSCLC is crucial for developing new treatment strategies.MethodsFinally, machine learning algorithms were employed to construct a risk signature with strong predictive power across multiple independent cohorts. …”
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Subtidal seagrass and blue carbon mapping at the regional scale: a cloud-native multi-temporal Earth Observation approach by Mar Roca, Chengfa Benjamin Lee, Avi Putri Pertiwi, Alina Blume, Isabel Caballero, Gabriel Navarro, Dimosthenis Traganos

“…Our synoptic tool uses Sentinel-2 A/B satellite imagery at 10 m spatial resolution to generate a multi-temporal composite (2016–2022) of the Balearic Islands’ coastal waters within the Google Earth Engine cloud computing platform, optimizing image processing and highlighting the importance of a high-resolution bathymetric dataset to increase seagrass mapping accuracies. Machine learning algorithms have been applied to perform seagrass detection, obtaining a seagrass cartography up to 30 m of depth, estimating 505.6 km2 of seagrass habitat extent. …”
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Smart diabetes management: remote monitoring and predictive health insights by K.S. Smelyakov, I.A. Lurin, K.V. Misiura, A.S. Chupryna, T.V. Tyzhnenko, O.D. Dolhanenko, V.M. Repikhov

“…The use of deep learning and neural network algorithms enhances the accuracy of these predictions by capturing complex data trends over time. …”
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Artificial intelligence techniques applications in the wastewater: A comprehensive review by Zakur Yahya, Márquez Fausto, Al-Taie Ali, Alsaidi Saif, Alsadoon Abeer, Mirashrafi Seyed Bagher, Flaih Laith, Zakoor Yousif

“…The critical gaps and the future directions in the (AI) algorithms for the wastewater treatment, including the explain ability of the data-driven models or transfer Learning processes and reinforcement learning, are also addressed.…”
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Generative Adversarial Network for Damage Identification in Civil Structures by Zahra Rastin, Gholamreza Ghodrati Amiri, Ehsan Darvishan

“…Most of the proposed methods employ supervised algorithms that require data from different damaged states of a structure in order to monitor its health conditions. …”
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STUDY ON THE USE OF COMPUTER BASED INFORMATION SYSTEMS IN PRODUCTION MANAGEMENT by Mirela PLESA (CHIRIACESCU)

“…The analysis will be performed using artificial intelligence methods that, through specific algorithms, allow the learning of new processing procedures, using data and previous knowledge collected, correlated with information obtained from meteorological services, to improve the making process. of decisions. …”
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Radiomic study on preoperative multi‐modal magnetic resonance images identifies IDH‐mutant TERT promoter‐mutant gliomas by Haoyu Wang, Shuxin Zhang, Xiang Xing, Qiang Yue, Wentao Feng, Siliang Chen, Jun Zhang, Dan Xie, Ni Chen, Yanhui Liu

“…A diagnostic model (multilayer perceptron classifier) for detecting the IDHmut pTERTmut gliomas was trained using an automatic machine‐learning algorithm named tree‐based pipeline optimization tool (TPOT). …”
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Improving Malaria diagnosis through interpretable customized CNNs architectures by Md. Faysal Ahamed, Md Nahiduzzaman, Golam Mahmud, Fariya Bintay Shafi, Mohamed Arselene Ayari, Amith Khandakar, M. Abdullah-Al-Wadud, S. M. Riazul Islam

“…The SPCNN achieved a precision of 99.38 $$\pm$$ 0.21%, recall of 99.37 $$\pm$$ 0.21%, F1 score of 99.37 $$\pm$$ 0.21%, accuracy of 99.37 ± 0.30%, and an area under the receiver operating characteristic curve (AUC) of 99.95 ± 0.01%, demonstrating its robustness in detecting malaria parasites. Furthermore, we employed various transfer learning (TL) algorithms, including VGG16, ResNet152, MobileNetV3Small, EfficientNetB6, EfficientNetB7, DenseNet201, Vision Transformer (ViT), Data-efficient Image Transformer (DeiT), ImageIntern, and Swin Transformer (versions v1 and v2). …”
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The global distribution and risk prediction of Anaplasmataceae species: a systematic review and geospatial modelling analysisResearch in context by Xiao-Bin Huang, Tian Tang, Jin-Jin Chen, Yuan-Yuan Zhang, Chen-Long Lv, Qiang Xu, Guo-Lin Wang, Ying Zhu, Yue-Hong Wei, Simon I. Hay, Li-Qun Fang, Wei Liu

“…Methods: We searched PubMed, Web of Science, bioRvix, and MedRvix for published articles to extract data on the detection of Anaplasmatacea species in vectors, animals, and humans from 1910 to 2022. …”
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Research status and progress on key technologies of intelligent orchard by CUI Hongwei, LU Xiaoxuan, YANG Yaqing, LU Xinyi, MA Hao, JI Jiangtao, JIN Xin, LI Xiuzhen, ZHAO Zimeng, ZENG Ningning

“…Harvesting robots, equipped with visual recognition technology and deep learning algorithms, can assess fruit maturity and perform harvesting tasks efficiently. …”
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Small RNAs as biomarkers to differentiate benign and malign prostate diseases: An alternative for transrectal punch biopsy of the prostate? by Lukas Markert, Jonas Holdmann, Claudia Klinger, Michael Kaufmann, Karin Schork, Michael Turewicz, Martin Eisenacher, Andreas Savelsbergh

“…In addition, machine-learning algorithms were used to identify a panel of 22 additional miRNAs, whose interaction makes it possible to differentiate the groups as well. …”
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Opportunities of Artificial Intelligence for Authentication and Assurance of Halal Products by Wayan Mahmudy, Diva Kurnianingtyas

“…With the help of high-speed computers, AI algorithms learn patterns in available data and perform assigned tasks with high accuracy and efficiency. …”
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Implications of artificial intelligence in periodontal treatment maintenance: a scoping review by Raafat Musief Sarakbi, Sudhir Rama Varma, Sudhir Rama Varma, Lovely Muthiah Annamma, Vinay Sivaswamy, Vinay Sivaswamy

“…Deep learning algorithms such as convolutional neural networks (CNNs) and segmentation techniques were analyzed for their diagnostic accuracy. …”
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Metabolomic Profiling Reveals Serum Tryptophan as a Potential Therapeutic Target for Systemic Lupus Erythematosus by Wang K, Zhu R, Xu M, Zhu K, Li J, Li C, Meng D, Chen H, Sun L

“…In the mouse model, tryptophan supplementation improved renal histology, reduced proteinuria, increased naïve T cells and central memory T cells, and decreased effector T cell frequencies in both peripheral blood and spleen.Conclusion: This study demonstrates the successful application of machine learning algorithms to metabolomics data for SLE classification and identifies tryptophan and beta-alanine as potential SLE-specific biomarkers. …”
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Registered report protocol: A scoping review to identify potential predictors as features for developing automated estimation of the probability of being frail in secondary care. by Dirk H van Dalen, Angèle P M Kerckhoffs, Esther de Vries

“…We aim to identify potential predictors that could be used as features for modeling algorithms on the basis of routine hospital EHR data to incorporate in an automated tool for estimating the probability of being frail.…”
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A comparative study of bone density in elderly people measured with AI and QCT by Min Guo, Min Guo, Yu Zhang, Yu Zhang, XinXin Gu, XinXin Gu, Xuhui Liu, Xuhui Liu, Fei Peng, Fei Peng, Zongjun Zhang, Zongjun Zhang, Mei Jing, Mei Jing, Yingxia Fu, Yingxia Fu

“…The linear regression fit between the R2 values of QCT and Bone Density AI for measuring lumbar spine BMD with different equipment ranged from 0.88 to 0.96, indicating a high degree of consistency between the two measurement methods across devices.ConclusionThis multicenter study pioneers a dual-validation framework to establish the clinical validity of deep learning-based BMD prediction algorithms using routine thoracic/abdominal CT scans. …”
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Odor prediction of whiskies based on their molecular composition by Satnam Singh, Doris Schicker, Helen Haug, Tilman Sauerwald, Andreas T. Grasskamp

“…Here, we combine fast automated analytical assessment tools with human sensory data of 11 experienced panelists and machine learning algorithms. …”
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Use of ICT to Confront COVID-19 by Yousry Saber El Gamal

“…Once a person is infected, AI capabilities can also be used to determine the probability of survival and the requirement of ICU treatment for COVID-19 patients. , AI techniques, particularly machine learning algorithms, can also be used to correlate the patient’s data parameters with a specific drug’s usage. …”
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