Leveraging transformers and explainable AI for Alzheimer's disease interpretability. by Humaira Anzum, Nabil Sadd Sammo, Shamim Akhter

“…To detect Alzheimer's disease, brain scans such as computed tomography (CT), magnetic resonance imaging (MRI), or positron emission tomography (PET) can be performed. Many researches have been undertaken to use computer vision on MRI images, and their accuracy ranges from 80-90%, new computer vision algorithms and cutting-edge transformers have the potential to improve this performance.We utilize advanced transformers and computer vision algorithms to enhance diagnostic accuracy, achieving an impressive 99% accuracy in categorizing Alzheimer's disease stages through translating RNA text data and brain MRI images in near-real-time. …”
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Role of Artificial Intelligence and Personalized Medicine in Enhancing HIV Management and Treatment Outcomes by Ashok Kumar Sah, Rabab H. Elshaikh, Manar G. Shalabi, Anass M. Abbas, Pranav Kumar Prabhakar, Asaad M. A. Babker, Ranjay Kumar Choudhary, Vikash Gaur, Ajab Singh Choudhary, Shagun Agarwal

“…The integration of artificial intelligence and personalized medicine is transforming HIV management by enhancing diagnostics, treatment optimization, and disease monitoring. …”
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Investigation of the use of a sensor bracelet for the presymptomatic detection of changes in physiological parameters related to COVID-19: an interim analysis of a prospective coho... by Stefanie Aeschbacher, David Conen, Diederick E Grobbee, Raphael Twerenbold, Thomas Lung, Theo Rispens, Jakob Kjellberg, Lorenz Risch, Martin Risch, Marianna Mitratza, Harald Renz, Spiros Denaxas, Billy Franks, Diederick Grobbee, Martina Rothenbühler, Janneke Wijgert, Santiago Montes, Richard Dobson, Hans Reitsma, Christian Simon, Titia Leurink, Charisma Hehakaya, Patricia Bruijning, Kirsten Grossmann, Ornella C Weideli, Marc Kovac, Fiona Pereira, Nadia Wohlwend, Corina Risch, Dorothea Hillmann, Daniel Leibovitz, Vladimir Kovacevic, Andjela Markovic, Paul Klaver, Timo B Brakenhoff, George S Downward, Ariel Dowling, Maureen Cronin, Brianna M Goodale, Brianna Goodale, Ornella Weideli, Regien Stokman, Hans Van Dijk, Eric Houtman, Jon Bouwman, Kay Hage, Lotte Smets, Marcel van Willigen, Maui Chodura, Niki de Vink, Tessa Heikamp, Timo Brakenhoff, Wendy van Scherpenzeel, Wout Aarts, Alison Kuchta, Antonella Chiucchiuini, Steve Emby, Annemarijn Douwes, George Downward, Nathalie Vigot, Pieter Stolk, Duco Veen, Daniel Oberski, Amos Folarin, Pablo Fernandez Medina, Eskild Fredslund

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Identification and validation of hub genes related to neutrophil extracellular traps-mediated cell damage and immune recruitment during abdominal aortic aneurysm by Chuanlong Lu, Heng Wang, Maolin Qiao, Runze Chang, Jinshan Chen, Lizheng Li, Keyi Fan, Sheng Yan, Ruijing Zhang, Honglin Dong

“…Following this, receiver operating characteristic curves were created to evaluate the diagnostic significance of these essential genes. Furthermore, the correlation between pivotal genes and immune cells was then examined using CIBERSORT and ssGSEA algorithms. …”
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Radiomic Analysis of Contrast‐Enhanced CT Predicts Recompensation in Hepatitis B‐Related Decompensated Cirrhosis by Qiaofeng Chen, Yuanping Fan, Kaini Wu, Tianpan Cai, Chunyu Lan, Mingju Yu, Yunfeng Fu, Qi Zhu, Jianhao Qiu, Xiaodong Zhou

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Unraveling genetic predisposition and oxidative stress in vitiligo development and the role of artificial intelligence (AI) in diagnosis and management by Kocić Hristina, Lotti Torello, Jevtović-Stoimenov Tatjana, Wollina Uwe, Valle Yan, Lukić Stevo, Klisić Aleksandra

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Lyme Disease Biobank: 10 years of 3 month follow-up visits from 2014 to 2023 by Elizabeth J. Horn, George Dempsey, Anna M. Schotthoefer, Matthew McArdle, Allison F. Weber, Cathy De Luca, Bobbi S. Pritt, Elizabeth L. Maloney

“…IntroductionLyme Disease Biobank (LDB) enrolls participants with signs and symptoms of early Lyme disease (LD) from endemic areas and makes samples available to researchers developing more accurate diagnostics. From 2014 to 23, 466 cases and 367 controls were enrolled on Long Island, NY, and in Central Wisconsin.MethodsThis study included 253 LDB participants who provided samples from an initial and a convalescent blood draw. …”
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Grid Search Based Hyperparameter-Tuned Deep Learning Model for Osteoporosis Diagnosis with Bi-Cubic Interpolation of X-Ray Images by Ruhul Amin, Md.Shamim Reza, Dewan Ahmed Muhtasim, Jungpil Shin, Md. Maniruzzaman, Md.Mahfujul Hasan

“…Even with the widespread use of deep learning (DL) and machine learning (ML) algorithms, the early diagnosis of osteoporosis patients can still be improved. …”
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Outpatient diagnosis of endogenous intoxication in surgery by A. A. Solomakha, A. P. Vlasov, V. I. Gorbachenko

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Molecular insights into ulcerative colitis and orbital inflammation by Kang Tan, Pei Liu, Zixuan Wu, Xi Long, Yunfeng Yu, Pengfei Jiang, Qinghua Peng

“…STAT1 was identified as a shared transcription factor influencing both diseases. Machine learning algorithms identified eight potential genes for diagnostic and therapeutic use, with CXCL10 emerging as a key player in the pathogenesis of NSOI and UC. …”
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Enhancing placental pathology detection with GAMatrix-YOLOv8 model by Weirui Wu, Zhifa Jiang, Jingwen Liu, Jiahui Ji, Xiaoyan Wei, Xiangyun Ye, Zhen Zhang

“…This provides a theoretical and technical foundation for further research into artificial intelligence-based pathological auxiliary diagnostic systems.…”
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Uncovering precision phenotype-biomarker associations in traumatic brain injury using topological data analysis. by Jessica L Nielson, Shelly R Cooper, John K Yue, Marco D Sorani, Tomoo Inoue, Esther L Yuh, Pratik Mukherjee, Tanya C Petrossian, Jesse Paquette, Pek Y Lum, Gunnar E Carlsson, Mary J Vassar, Hester F Lingsma, Wayne A Gordon, Alex B Valadka, David O Okonkwo, Geoffrey T Manley, Adam R Ferguson, TRACK-TBI Investigators

“…<h4>Methods and findings</h4>The Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Pilot multicenter study enrolled 586 acute TBI patients and collected diverse common data elements (TBI-CDEs) across the study population, including imaging, genetics, and clinical outcomes. …”
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Accurate bladder cancer diagnosis using ensemble deep leaning by Rana A. El-Atier, M. S. Saraya, Ahmed I. Saleh, Asmaa H. Rabie

“…The potential and use of artificial intelligence in urological diseases have been the subject of several research, as interest in artificial intelligence in medicine has grown recently. …”
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Machine learning applications for early detection of esophageal cancer: a systematic review by Farhang Hosseini, Farkhondeh Asadi, Hassan Emami, Rayan Ebnali Harari

“…Recent advances in machine learning (ML) techniques, particularly in computer vision, have demonstrated promising applications in medical image processing, assisting clinicians in making more accurate and faster diagnostic decisions. Given the significance of early detection of EC, this systematic review aims to summarize and discuss the current state of research on ML-based methods for the early detection of EC. …”
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The effectiveness of complex therapy in the treatment of nonspecific vaginitis in women of reproductive age by O.D. Ryazanova, G.I. Reznichenko

“…Materials and Methods. 160 women (100%) were examined, which were divided into 2 clinical groups with subgroups: the main group - 94 (58.8 %) patients with nonspecific vaginitis, who received treatment according to developed clinical diagnostic algorithms and schemes. The comparison group included 66 (41.2 %) patients with nonspecific vaginitis, who received treatment according to known protocols. …”
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The Emerging Role of Artificial Intelligence in Dermatology: A Systematic Review of Its Clinical Applications by Ernesto Martínez-Vargas, Jeaustin Mora-Jiménez, Sebastian Arguedas-Chacón, Josephine Hernández-López, Esteban Zavaleta-Monestel

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Mapping the use of artificial intelligence in medical education: a scoping review by Erwin Hernando Hernández Rincón, Daniel Jimenez, Lizeth Alexandra Chavarro Aguilar, Juan Miguel Pérez Flórez, Álvaro Enrique Romero Tapia, Claudia Liliana Jaimes Peñuela

“…Abstract Introduction The integration of artificial intelligence (AI) in healthcare has transformed clinical practices and medical education, with technologies like diagnostic algorithms and clinical decision support increasingly incorporated into curricula. …”
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Deep Learning Approaches for Retinal Disease Identification in Fundus Imaging: A Comprehensive Overview by Ismael Abdulkareem Ali, Sozan Abdullah Mahmood

“…Therefore, deep learning diagnostic techniques are widely used for identifying eye diseases and play a crucial role in diagnosing vision-related problems. …”
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A Clinical Case of Two-Stage Visual-Functional Rehabilitation of a Patient with Postinfectious Central Corneal Opacity in Combination with Mixed Astigmatism Using Laser Keratorefra... by N. V. Maychuk, I. A. Mushkova, M. R. Obraztsova

“…To determine the possibility of surgical treatment of the patient, a complex of General ophthalmology was performed (checking visual acuity near and far in natural conditions and in conditions of drug-induced mydriasis, IOP measurement, autorefractometry, examination of visual fields, A- and Bscanning, ophthalmoscopy with examination of the Central and peripheral areas of the fundus), as well as special (keratopography and Scheimpflug camera examination, optical coherence tomography (OCT) of the anterior segment of the eye with analysis of the epithelial map, quantitative assessment (Schirmer test-1) and qualitative (tear film rupture time) lachrymal products, assessment of the epithelium state when stained with vital dyes and infrared meibography) research methods. To determine the activity of herpetic infection, PCR diagnostics were performed. …”
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Predicting the subclinical carotid atherosclerosis in overweight and obese patients using a machine learning model by D. V. Gavrilov, T. Yu. Kuznetsova, M. A. Druzhilov, I. N. Korsakov, A. V. Gusev

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