Validation of Quantitative Ultrasound and Texture Derivative Analyses-Based Model for Upfront Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer by Adrian Wai Chan, Lakshmanan Sannachi, Daniel Moore-Palhares, Archya Dasgupta, Sonal Gandhi, Rossanna Pezo, Andrea Eisen, Ellen Warner, Frances C. Wright, Nicole Look Hong, Ali Sadeghi-Naini, Mia Skarpathiotakis, Belinda Curpen, Carrie Betel, Michael C. Kolios, Maureen Trudeau, Gregory J. Czarnota

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The effect of controlling the distance and height of the board in the development of some cognitive abilities (sense-kinetic) and Learnning of the accuracy of the correction skill... by Dr. Ali Fattah M. Omar Mohammed

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Remaining Useful Life Estimation through Deep Learning Partial Differential Equation Models: A Framework for Degradation Dynamics Interpretation Using Latent Variables by Sergio Cofre-Martel, Enrique Lopez Droguett, Mohammad Modarres

“…For the past decade, researchers have explored the application of deep learning (DL) regression algorithms to predict the system’s health state behavior based on sensor readings from the monitoring system. …”
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Association between glucose-to-albumin ratio and ischemic stroke risk in patients with coronary heart disease: a machine learning-based predictive model analysis by Ling Hou, Yuanhong Li, Qianfei Liu

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Simultaneously detecting the intensity and position of Southwestern Atlantic Ocean Frontal Zones from satellite-derived SST by a multi-task deep learning model by Zhi Wang, Guangyu Yang, Yanchen Guo, Zhenkuan Pan, Ge Chen, Chunyong Ma

“…To address these limitations, we propose a multi-task deep learning semantic segmentation model, named Multi-Task Attention D-LinkNet (MTAD-LinkNet), which utilizes D-LinkNet as the backbone. …”
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An interpretable ensemble model combining handcrafted radiomics and deep learning for predicting the overall survival of hepatocellular carcinoma patients after stereotactic body r... by Yi Chen, David Pasquier, Damon Verstappen, Henry C. Woodruff, Philippe Lambin

“…This study seeks to develop a robust predictive model by integrating radiomics and deep learning features with clinical data to predict 2-year survival in HCC patients treated with stereotactic body radiation therapy (SBRT). …”
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Comparison of Learning Curves and Clinical Outcomes in Unilateral Biportal Endoscopic Spinal Surgery Versus Percutaneous Transforaminal Endoscopic Surgery: A Cumulative Sum Analysi... by Yuan S, Chen R, Mei Y, Fan N, Wang T, Wang A, Du P, Xi Y, Zang L

“…CUSUM analysis was conducted to assess the learning curve, with cutoff points used to categorize the early and late phases. …”
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Enhancing patient-specific deep learning based segmentation for abdominal magnetic resonance imaging-guided radiation therapy: A framework conditioned on prior segmentation by Francesca De Benetti, Nikolaos Delopoulos, Claus Belka, Stefanie Corradini, Nassir Navab, Thomas Wendler, Shadi Albarqouni, Guillaume Landry, Christopher Kurz

“…Background and purpose:: Conventionally, the contours annotated during magnetic resonance-guided radiation therapy (MRgRT) planning are manually corrected during the RT fractions, which is a time-consuming task. Deep learning-based segmentation can be helpful, but the available patient-specific approaches require training at least one model per patient, which is computationally expensive. …”
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Learning Transversus Abdominis Activation in Older Adults with Chronic Low Back Pain Using an Ultrasound-Based Wearable: A Randomized Controlled Pilot Study by Luis Perotti, Oskar Stamm, Hannah Strohm, Jürgen Jenne, Marc Fournelle, Nils Lahmann, Ursula Müller-Werdan

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A CT-Based Deep Learning Radiomics Scoring System for Predicting the Prognosis to Repeat TACE in Patients with Hepatocellular Carcinoma: A Multicenter Cohort Study by Dai Y, Zhao S, Wu Q, Zhang J, Zeng X, Jiang H

“…After Cox regression analysis of these characteristics, the scoring system (HBsAg-Radscore-DLscore, HRD) was significantly associated with OS in patients with HCC, and was superior to the traditional ART score and ABCR score between high and low-risk patients.Conclusion: Deep learning and radiomics had good performance in predicting the OS of patients with HCC treated with repeated TACE. …”
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Evaluating the value of machine learning models for predicting hematoma expansion in acute spontaneous intracerebral hemorrhage based on CT imaging features of hematomas and surrou... by Tianyu Yang, Tianyu Yang, Zhen Zhao, Yan Gu, Shengkai Yang, Yonggang Zhang, Lei Li, Ting Wang, Zhongchang Miao

“…Additionally, it aims to extract imaging features for developing machine learning models to predict hematoma expansion in acute spontaneous intracerebral hemorrhage (sICH).MethodsData from 183 patients with acute spontaneous hemorrhage, treated at Lianyungang Hospital Affiliated to Xuzhou Medical University between January 2020 and December 2023, were retrospectively analyzed. …”
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Multi-omics analysis and experiments uncover the function of cancer stemness in ovarian cancer and establish a machine learning-based model for predicting immunotherapy responses by Zhibing Liu, Zhibing Liu, Lei Han, Xiaoyu Ji, Xiaole Wang, Jinbo Jian, Yujie Zhai, Yingjiang Xu, Feng Wang, Xiuwen Wang, Fangling Ning

“…This identified gene set underpinned the development of the CSI, a groundbreaking tool leveraging advanced machine learning to predict prognosis and immunotherapy responses in ovarian cancer patients. …”
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Recovery effect of transplantation of neural stem cells derived from bone marrow stromal cells on learning and memory dysfunction induced by temporal lobe epilepsy in rats by SHEN Wei-gao, LIU Yan-bo, LI Shu-bo, HE Xin

“…In order to research the recovery effect of neural stem cells (NSCs) derived from bone marrow stromal cells (BMSCs) to transplant into hippocampus of rats with epilepsy induced by kanic acid on their learning and memory dysfunction and approach the theory about NSCs transplantation treating epilepsy, the BMSCs were segregated, cultured and differentiated into NSCs in vitro at first, and secondly the rat models of epilepsy were established with kanic acid. …”
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Antioxidant, anti-acetylcholinesterase, and anti-amyloid-β peptide aggregations of hispolon and its analogs in vitro and improved learning and memory functions in scopolamine-induc... by Chang-Hang Yang, Cai-Wei Li, Yi-Yan Sie, Liang-Chieh Chen, Yu-Hsiang Yuan, Wen-Chi Hou

“…Conclusion The hispolon in the fungus sang-huang might be beneficial to develop functional foods or as lead compounds for treating degenerative disorders.…”
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Predicting Gestational Diabetes Mellitus in the first trimester using machine learning algorithms: a cross-sectional study at a hospital fertility health center in Iran by Somayeh Kianian Bigdeli, Marjan Ghazisaedi, Seyed Mohammad Ayyoubzadeh, Sedigheh Hantoushzadeh, Marjan Ahmadi

“…This model will help obstetricians and gynecologists make appropriate decisions for treating and preventing GDM complications. Methods This applied descriptive study was conducted at the fertility health center of Vali-e-Asr Hospital in Tehran, Iran. …”
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Multi-headed ensemble residual CNN: A powerful tool for fibroblast growth factor prediction by Naif Almusallam, Farman Ali, Harish Kumar, Tamim Alkhalifah, Fahad Alturise, Abdullah Almuhaimeed

“…Fibroblast Growth Factor (FGF) performs a significant role in the repair, nervous system, development, and maintenance, making it a promising target for treating neurological diseases such as Parkinson's, stroke, Alzheimer's, and Huntington's disorders. …”
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End-to-end neural automatic speech recognition system for low resource languages by Sami Dhahbi, Nasir Saleem, Sami Bourouis, Mouhebeddine Berrima, Elena Verdú

“…The rising popularity of end-to-end (E2E) automatic speech recognition (ASR) systems can be attributed to their ability to learn complex speech patterns directly from raw data, eliminating the need for intricate feature extraction pipelines and handcrafted language models. …”
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Disseminate Cutaneous Vasculitis Associated with Durvalumab Treatment—Case Report, Mini-Review on Cutaneous Side Effects of Immune Checkpoint Inhibitor Therapies with Machine Learn... by Gyula Laszlo Fekete, Laszlo Barna Iantovics, Júlia Edit Fekete, Laszlo Fekete

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Belirtili, Belirtisiz ve Takısız Ad Tamlaması in L2-Turkish Evidence From the Learning Difficulties of L1-Greek-speaking learners and Teaching Suggestions by Vasiliki Mavridou

“…However the current case in L2-Turkish teaching/ learning is that the AT category and its sub-sets are treated differently in L2-Turkish grammars and teaching/ learning coursebook material, which is a main reason why AT poses a major acquisition problem for adult L2-Turkish learners. …”
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Dynamic, Interpretable, Machine Learning–Based Outcome Prediction as a New Emerging Opportunity in Acute Ischemic Stroke Patient Care: A Proof-of-Concept Study by Ivan Petrović, Sava Njegovan, Olivera Tomašević, Dmitar Vlahović, Sonja Rajić, Željko Živanović, Isidora Milosavljević, Ana Balenović, Nikola Jorgovanović

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