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Wyszukujesz frazę ""DEEP learning"" wg kryterium: Temat


Starter badań:

Tytuł:
Improving the Prognostic Evaluation Precision of Hospital Outcomes for Heart Failure Using Admission Notes and Clinical Tabular Data: Multimodal Deep Learning Model.
Autorzy:
Gao Z; Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China.
Liu X; Center for Artificial Intelligence in Medicine, The General Hospital of People's Liberation Army, Beijing, China.
Kang Y; Department of Cardiology, West China Hospital, Sichuan University, Chengdu, China.
Hu P; Center for Artificial Intelligence in Medicine, The General Hospital of People's Liberation Army, Beijing, China.
Zhang X; Department of Cardiology, West China Hospital, Sichuan University, Chengdu, China.
Yan W; Center for Artificial Intelligence in Medicine, The General Hospital of People's Liberation Army, Beijing, China.
Yan M; Center for Artificial Intelligence in Medicine, The General Hospital of People's Liberation Army, Beijing, China.
Yu P; Department of Cardiology, West China Hospital, Sichuan University, Chengdu, China.
Zhang Q; Department of Cardiology, West China Hospital, Sichuan University, Chengdu, China.
Xiao W; Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China.
Zhang Z; Center for Artificial Intelligence in Medicine, The General Hospital of People's Liberation Army, Beijing, China.
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Źródło:
Journal of medical Internet research [J Med Internet Res] 2024 May 02; Vol. 26, pp. e54363. Date of Electronic Publication: 2024 May 02.
Typ publikacji:
Journal Article; Research Support, Non-U.S. Gov't
MeSH Terms:
Heart Failure*/mortality
Heart Failure*/therapy
Deep Learning*
Humans ; Male ; Female ; Prognosis ; Aged ; Retrospective Studies ; Middle Aged ; Electronic Health Records ; Hospitalization/statistics & numerical data ; Hospital Mortality ; Aged, 80 and over
Czasopismo naukowe
Tytuł:
Deep learning-assisted flavonoid-based fluorescent sensor array for the nondestructive detection of meat freshness.
Autorzy:
Li M; State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi 214122, PR China; School of Food Science and Technology, Jiangnan University, Wuxi 214122, PR China.
Xu J; Key Laboratory of Molecular Recognition and Sensing, College of Biological, Chemical Sciences and Engineering, Jiaxing University, Jiaxing 314001, PR China.
Peng C; State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi 214122, PR China; School of Food Science and Technology, Jiangnan University, Wuxi 214122, PR China; School of Life Science and Health Engineering, Jiangnan University, Wuxi 214122, PR China; International Joint Laboratory On Food Safety, Jiangnan University, Wuxi 214122, PR China. Electronic address: .
Wang Z; State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi 214122, PR China; School of Food Science and Technology, Jiangnan University, Wuxi 214122, PR China; School of Life Science and Health Engineering, Jiangnan University, Wuxi 214122, PR China; International Joint Laboratory On Food Safety, Jiangnan University, Wuxi 214122, PR China.
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Źródło:
Food chemistry [Food Chem] 2024 Jul 30; Vol. 447, pp. 138931. Date of Electronic Publication: 2024 Mar 04.
Typ publikacji:
Journal Article
MeSH Terms:
Flavonoids*
Deep Learning*
Nitrogen ; Meat/analysis ; Neural Networks, Computer ; Coloring Agents
Czasopismo naukowe
Tytuł:
Deep learning approach to femoral AVN detection in digital radiography: differentiating patients and pre-collapse stages.
Autorzy:
Rakhshankhah N; Department of Radiology and Health Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran.
Abbaszadeh M; Department of Orthopedic Surgery, Faculty of Medicine, Baqiyatallah University of Medical Sciences, Tehran, Iran.
Kazemi A; Department of Radiology and Health Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran.
Rezaei SS; Student Research Committee, Baqiyatallah University of Medical Sciences, Tehran, Iran.
Roozpeykar S; Department of Radiology and Health Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran. .
Arabfard M; Chemical Injuries Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran. .
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Źródło:
BMC musculoskeletal disorders [BMC Musculoskelet Disord] 2024 Jul 16; Vol. 25 (1), pp. 547. Date of Electronic Publication: 2024 Jul 16.
Typ publikacji:
Journal Article
MeSH Terms:
Deep Learning*
Femur Head Necrosis*/diagnostic imaging
Humans ; Female ; Male ; Middle Aged ; Adult ; Aged ; Magnetic Resonance Imaging/methods ; Young Adult ; Diagnosis, Differential ; Radiographic Image Interpretation, Computer-Assisted/methods ; Adolescent
Czasopismo naukowe
Tytuł:
Deep transfer learning for detection of breast arterial calcifications on mammograms: a comparative study.
Autorzy:
Mobini N; Department of Biomedical Sciences for Health, Università degli Studi di Milano, Milan, Italy.
Capra D; Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Milan, Italy. .
Colarieti A; Radiology Unit, IRCCS Policlinico San Donato, Via Morandi 30, 20097, San Donato Milanese, Italy.
Zanardo M; Radiology Unit, IRCCS Policlinico San Donato, Via Morandi 30, 20097, San Donato Milanese, Italy.
Baselli G; Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milan, Italy.
Sardanelli F; Radiology Unit, IRCCS Policlinico San Donato, Via Morandi 30, 20097, San Donato Milanese, Italy.; Lega Italiana per la lotta contro i Tumori (LILT) Milano Monza Brianza, Milan, Italy.
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Źródło:
European radiology experimental [Eur Radiol Exp] 2024 Jul 15; Vol. 8 (1), pp. 80. Date of Electronic Publication: 2024 Jul 15.
Typ publikacji:
Journal Article; Comparative Study
MeSH Terms:
Mammography*/methods
Deep Learning*
Breast Diseases*/diagnostic imaging
Humans ; Female ; Retrospective Studies ; Middle Aged ; Aged ; Adult ; Breast/diagnostic imaging ; Vascular Calcification/diagnostic imaging ; Calcinosis/diagnostic imaging ; Radiographic Image Interpretation, Computer-Assisted/methods
Czasopismo naukowe
Tytuł:
Cross-attention enables deep learning on limited omics-imaging-clinical data of 130 lung cancer patients.
Autorzy:
Verma S; School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, UK.
Magazzù G; York St John University, York, UK.
Eftekhari N; The Alan Turing Institute, London, UK.
Lou T; Gateshead Health NHS Foundation Trust, Gateshead, UK.
Gilhespy A; South Tyneside and Sunderland NHS Foundation Trust, Sunderland, UK.
Occhipinti A; School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, UK; Centre for Digital Innovation, Teesside University, Middlesbrough, UK; National Horizons Centre, Teesside University, Darlington, UK.
Angione C; School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, UK; Centre for Digital Innovation, Teesside University, Middlesbrough, UK; National Horizons Centre, Teesside University, Darlington, UK. Electronic address: .
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Źródło:
Cell reports methods [Cell Rep Methods] 2024 Jul 15; Vol. 4 (7), pp. 100817. Date of Electronic Publication: 2024 Jul 08.
Typ publikacji:
Journal Article
MeSH Terms:
Lung Neoplasms*/genetics
Lung Neoplasms*/diagnostic imaging
Lung Neoplasms*/pathology
Deep Learning*
Carcinoma, Non-Small-Cell Lung*/genetics
Carcinoma, Non-Small-Cell Lung*/diagnostic imaging
Carcinoma, Non-Small-Cell Lung*/pathology
Humans ; Tomography, X-Ray Computed/methods ; Biomarkers, Tumor/genetics ; Prognosis ; Male ; Female ; Gene Expression Regulation, Neoplastic ; Transcriptome
Czasopismo naukowe
Tytuł:
nBEST: Deep-learning-based non-human primates Brain Extraction and Segmentation Toolbox across ages, sites and species.
Autorzy:
Zhong T; School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing and Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China.
Wu X; School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing and Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China.
Liang S; School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing and Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China.
Ning Z; School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing and Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China.
Wang L; Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, USA.
Niu Y; Yunnan Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, China.
Yang S; College of Veterinary Medicine, South China Agricultural University, Guangzhou, China.
Kang Z; Department of Radiology, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Feng Q; School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing and Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China.
Li G; Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, USA. Electronic address: gang_.
Zhang Y; School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing and Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China. Electronic address: .
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Źródło:
NeuroImage [Neuroimage] 2024 Jul 15; Vol. 295, pp. 120652. Date of Electronic Publication: 2024 May 24.
Typ publikacji:
Journal Article
MeSH Terms:
Deep Learning*
Brain*/diagnostic imaging
Brain*/anatomy & histology
Magnetic Resonance Imaging*/methods
Animals ; Image Processing, Computer-Assisted/methods ; Macaca mulatta ; Neuroimaging/methods ; Pan troglodytes/anatomy & histology ; Aging/physiology
Czasopismo naukowe
Tytuł:
Deep learning model based on endoscopic images predicting treatment response in locally advanced rectal cancer undergo neoadjuvant chemoradiotherapy: a multicenter study.
Autorzy:
Zhang J; Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, No. 16, Jiangsu Road, Shinan District, Qingdao, 266003, China.
Liu R; Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, No. 16, Jiangsu Road, Shinan District, Qingdao, 266003, China.
Wang X; Graduate School for Elite Engineers, Shandong University, Jinan, China.
Zhang S; Department of Gastric and Colorectal Surgery, General Surgery Center, The First Hospital of Jilin University, Changchun, China.
Shao L; CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
Liu J; Department of Gastroenterology, The Affiliated Hospital of Qingdao University, Qingdao, China.
Zhao J; Department of Gastroenterology, Endoscopy Center, The First Hospital of Jilin University, Changchun, China.
Wang Q; Department of Gastric and Colorectal Surgery, General Surgery Center, The First Hospital of Jilin University, Changchun, China.
Tian J; CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China. .; School of Engineering Medicine, Beihang University, Beijing, 100191, China. .
Lu Y; Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, No. 16, Jiangsu Road, Shinan District, Qingdao, 266003, China. .
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Źródło:
Journal of cancer research and clinical oncology [J Cancer Res Clin Oncol] 2024 Jul 13; Vol. 150 (7), pp. 350. Date of Electronic Publication: 2024 Jul 13.
Typ publikacji:
Journal Article; Multicenter Study; Observational Study
MeSH Terms:
Rectal Neoplasms*/therapy
Rectal Neoplasms*/pathology
Rectal Neoplasms*/diagnostic imaging
Deep Learning*
Neoadjuvant Therapy*/methods
Humans ; Male ; Female ; Middle Aged ; Retrospective Studies ; Aged ; Chemoradiotherapy/methods ; Adult ; Treatment Outcome ; Chemoradiotherapy, Adjuvant/methods
Czasopismo naukowe
Tytuł:
Predictive value of MRI-based deep learning model for lymphovascular invasion status in node-negative invasive breast cancer.
Autorzy:
Liang R; Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, West Huan-Hu Road, Ti Yuan Bei, Hexi District, Tianjin, 300060, People's Republic of China.; Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin, People's Republic of China.
Li F; Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, West Huan-Hu Road, Ti Yuan Bei, Hexi District, Tianjin, 300060, People's Republic of China.; Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin, People's Republic of China.
Yao J; Department of Physiology and Biochemistry, School of Fundamental Medicine, Shanghai University of Medicine and Health Sciences, Shanghai, People's Republic of China.
Tong F; Department of Physiology and Biochemistry, School of Fundamental Medicine, Shanghai University of Medicine and Health Sciences, Shanghai, People's Republic of China.; Institute of Wound Prevention and Treatment, Shanghai University of Medicine and Health Sciences, Shanghai, People's Republic of China.; Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital, Shanghai, People's Republic of China.
Hua M; Department of Radiology, Chest Hospital, Tianjin University, Tianjin, People's Republic of China.
Liu J; Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, West Huan-Hu Road, Ti Yuan Bei, Hexi District, Tianjin, 300060, People's Republic of China.; Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin, People's Republic of China.
Shi C; Department of Physiology and Biochemistry, School of Fundamental Medicine, Shanghai University of Medicine and Health Sciences, Shanghai, People's Republic of China.
Sui L; Department of Physiology and Biochemistry, School of Fundamental Medicine, Shanghai University of Medicine and Health Sciences, Shanghai, People's Republic of China.
Lu H; Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, West Huan-Hu Road, Ti Yuan Bei, Hexi District, Tianjin, 300060, People's Republic of China. luhong_.; Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin, People's Republic of China. luhong_.
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Źródło:
Scientific reports [Sci Rep] 2024 Jul 13; Vol. 14 (1), pp. 16204. Date of Electronic Publication: 2024 Jul 13.
Typ publikacji:
Journal Article
MeSH Terms:
Breast Neoplasms*/pathology
Breast Neoplasms*/diagnostic imaging
Deep Learning*
Magnetic Resonance Imaging*/methods
Lymphatic Metastasis*/diagnostic imaging
Lymph Nodes*/pathology
Lymph Nodes*/diagnostic imaging
Neoplasm Invasiveness*
Humans ; Female ; Middle Aged ; Retrospective Studies ; Adult ; Aged ; Predictive Value of Tests
Czasopismo naukowe
Tytuł:
Medical intelligence using PPG signals and hybrid learning at the edge to detect fatigue in physical activities.
Autorzy:
Liu P; Department of Physical Education and Teaching, Hebei Finance University, Baoding, 071051, China.
Song Y; Department of Physical Education and Teaching, Hebei Finance University, Baoding, 071051, China.; Faculty of Sport Sciences & Recreation, Universiti Teknologi MARA (UiTM), 40450, Shah Alam, Selangor, Malaysia.
Yang X; Graduate School, Angeles University Foundation, 2009, Angeles, Philippines.; Shandong Provincial University Laboratory for Protected Horticulture, Weifang University of Science and Technology, Shouguang, Weifang, 262700, Shandong, China.
Li D; Graduate School, Angeles University Foundation, 2009, Angeles, Philippines.; Shandong Provincial University Laboratory for Protected Horticulture, Weifang University of Science and Technology, Shouguang, Weifang, 262700, Shandong, China.
Khosravi M; School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran. .; Shandong Provincial University Laboratory for Protected Horticulture, Weifang University of Science and Technology, Shouguang, Weifang, 262700, Shandong, China. .
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Źródło:
Scientific reports [Sci Rep] 2024 Jul 12; Vol. 14 (1), pp. 16149. Date of Electronic Publication: 2024 Jul 12.
Typ publikacji:
Journal Article
MeSH Terms:
Fatigue*/diagnosis
Fatigue*/physiopathology
Photoplethysmography*/methods
Exercise*/physiology
Deep Learning*
Humans ; Neural Networks, Computer ; Male ; Female ; Signal Processing, Computer-Assisted ; Young Adult
Czasopismo naukowe
Tytuł:
A Computed Tomography-Based Fracture Prediction Model With Images of Vertebral Bones and Muscles by Employing Deep Learning: Development and Validation Study.
Autorzy:
Kong SH; Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.
Cho W; Kim Jaechul Graduate School of AI, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.
Park SB; Department of Neurosurgery, Seoul National University Boramae Hospital, Seoul, Republic of Korea.
Choo J; Kim Jaechul Graduate School of AI, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.
Kim JH; Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
Kim SW; Department of Internal Medicine, Seoul National University Boramae Hospital, Seoul, Republic of Korea.
Shin CS; Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
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Źródło:
Journal of medical Internet research [J Med Internet Res] 2024 Jul 12; Vol. 26, pp. e48535. Date of Electronic Publication: 2024 Jul 12.
Typ publikacji:
Journal Article; Validation Study
MeSH Terms:
Deep Learning*
Tomography, X-Ray Computed*/methods
Spinal Fractures*/diagnostic imaging
Humans ; Female ; Male ; Aged ; Retrospective Studies ; Middle Aged ; Longitudinal Studies ; Spine/diagnostic imaging ; Muscle, Skeletal/diagnostic imaging ; Muscle, Skeletal/injuries
Czasopismo naukowe
Tytuł:
YOLO-V5 based deep learning approach for tooth detection and segmentation on pediatric panoramic radiographs in mixed dentition.
Autorzy:
Beser B; Department of Orthodontics, Faculty of Dentistry, Recep Tayyip Erdogan University, Rize, Turkey.
Reis T; Pedodontics, Private Practice, Trabzon, Turkey.
Berber MN; Department of Orthodontics, Faculty of Dentistry, Recep Tayyip Erdogan University, Rize, Turkey.
Topaloglu E; Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Inonu University, Malatya, Turkey.
Gungor E; Department of Pedodontics, Faculty of Dentistry, Inonu University, Malatya, Turkey.
Kılıc MC; Department of Pedodontics, Faculty of Dentistry, Beykent University, Istanbul, Turkey.
Duman S; Department of Pedodontics, Faculty of Dentistry, Inonu University, Malatya, Turkey.
Çelik Ö; Department of Mathematics-Computer, Faculty of Science, Eskisehir Osmangazi University, Eskisehir, Turkey.
Kuran A; Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Kocaeli University, İzmit, Kocaeli, 41190, Turkey. .
Bayrakdar IS; Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, Eskişehir, Turkey.
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Źródło:
BMC medical imaging [BMC Med Imaging] 2024 Jul 11; Vol. 24 (1), pp. 172. Date of Electronic Publication: 2024 Jul 11.
Typ publikacji:
Journal Article
MeSH Terms:
Radiography, Panoramic*/methods
Deep Learning*/standards
Tooth*/diagnostic imaging
Dentition, Mixed*
Pediatric Dentistry*/methods
Humans ; Child, Preschool ; Child ; Adolescent ; Male ; Female
Czasopismo naukowe
Tytuł:
Nano fuzzy alarming system for blood transfusion requirement detection in cancer using deep learning.
Autorzy:
Rady Raz N; Department of Artificial Intelligence in Medical Sciences, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran.
Anoushirvani AA; Department of Artificial Intelligence in Medical Sciences, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran. anoush_.; Department of Internal Medicine, Firoozgar General Hospital, School of Medicine, Iran University of Medical Sciences, Tehran, Iran. anoush_.
Rahimian N; Department of Internal Medicine, Firoozgar General Hospital, School of Medicine, Iran University of Medical Sciences, Tehran, Iran.
Ghoerishi M; Department of Internal Medicine, Firoozgar General Hospital, School of Medicine, Iran University of Medical Sciences, Tehran, Iran.
Alibeik N; Department of Internal Medicine, Firoozgar General Hospital, School of Medicine, Iran University of Medical Sciences, Tehran, Iran.
Sajadi Rad M; Department of Radiation Oncology, Firoozgar General Hospital, School of Medicine, Iran University of Medical Sciences, Tehran, Iran.
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Źródło:
Scientific reports [Sci Rep] 2024 Jul 10; Vol. 14 (1), pp. 15958. Date of Electronic Publication: 2024 Jul 10.
Typ publikacji:
Journal Article
MeSH Terms:
Deep Learning*
Neoplasms*/therapy
Blood Transfusion*/methods
Fuzzy Logic*
Neural Networks, Computer*
Humans ; Algorithms ; Female ; Male
Czasopismo naukowe
Tytuł:
Classification of osteoarthritic and healthy cartilage using deep learning with Raman spectra.
Autorzy:
Kok YE; School of Computer Science, University of Nottingham, Nottingham, NG8 1BB, UK. .
Crisford A; Institute of Life Sciences and Department of Chemistry, University of Southampton, Southampton, SO17 1BJ, UK.
Parkes A; School of Computer Science, University of Nottingham, Nottingham, NG8 1BB, UK.
Venkateswaran S; Precision Healthcare University Research Institute, Queen Mary University of London, London, E1 1HH, UK.
Oreffo R; Bone and Joint Research Group, Centre for Human Development, Stem Cells and Regeneration, Institute of Developmental Sciences, University of Southampton, Southampton, SO16 6YD, UK.
Mahajan S; Institute of Life Sciences and Department of Chemistry, University of Southampton, Southampton, SO17 1BJ, UK.
Pound M; School of Computer Science, University of Nottingham, Nottingham, NG8 1BB, UK.
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Źródło:
Scientific reports [Sci Rep] 2024 Jul 10; Vol. 14 (1), pp. 15902. Date of Electronic Publication: 2024 Jul 10.
Typ publikacji:
Journal Article
MeSH Terms:
Spectrum Analysis, Raman*/methods
Deep Learning*
Osteoarthritis*/classification
Osteoarthritis*/diagnosis
Neural Networks, Computer*
Humans ; Female ; Male ; Cartilage, Articular/pathology ; Middle Aged ; Aged ; Osteoporosis/diagnosis ; Support Vector Machine
Czasopismo naukowe
Tytuł:
Innovative infrastructure to access Brazilian fungal diversity using deep learning.
Autorzy:
Chaves T; Brazilian National Institute for Digital Convergence-INCoD, Universidade Federal de Santa Catarina, Florianópolis, Santa Catarina, Brazil.
Santos Xavier J; Institute of Agricultural Science, Universidade Federal dos Vales do Jequitinhonha e Mucuri, Unaí, Minas Gerais, Brazil.
Gonçalves Dos Santos A; Brazilian National Institute for Digital Convergence-INCoD, Universidade Federal de Santa Catarina, Florianópolis, Santa Catarina, Brazil.
Martins-Cunha K; MIND.Funga/MICOLAB, Department of Botany, Universidade Federal de Santa Catarina, Florianópolis, Santa Catarina, Brazil.
Karstedt F; MIND.Funga/MICOLAB, Department of Botany, Universidade Federal de Santa Catarina, Florianópolis, Santa Catarina, Brazil.
Kossmann T; MIND.Funga/MICOLAB, Department of Botany, Universidade Federal de Santa Catarina, Florianópolis, Santa Catarina, Brazil.
Sourell S; MIND.Funga/MICOLAB, Department of Botany, Universidade Federal de Santa Catarina, Florianópolis, Santa Catarina, Brazil.
Leopoldo E; MIND.Funga/MICOLAB, Department of Botany, Universidade Federal de Santa Catarina, Florianópolis, Santa Catarina, Brazil.
Fortuna Ferreira MN; Brazilian National Institute for Digital Convergence-INCoD, Universidade Federal de Santa Catarina, Florianópolis, Santa Catarina, Brazil.
Farias R; Brazilian National Institute for Digital Convergence-INCoD, Universidade Federal de Santa Catarina, Florianópolis, Santa Catarina, Brazil.
Titton M; MIND.Funga/MICOLAB, Department of Botany, Universidade Federal de Santa Catarina, Florianópolis, Santa Catarina, Brazil.
Alves-Silva G; MIND.Funga/MICOLAB, Department of Botany, Universidade Federal de Santa Catarina, Florianópolis, Santa Catarina, Brazil.
Bittencourt F; MIND.Funga/MICOLAB, Department of Botany, Universidade Federal de Santa Catarina, Florianópolis, Santa Catarina, Brazil.
Bortolini D; Department of Microbiology, Institute of Biological Sciences, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Minas Gerais, Brazil.
Gumboski EL; Department of Biological Sciences, Regional University of Joinville (UNIVILLE), Joinville, Santa Catarina, Brazil.
von Wangenheim A; Brazilian National Institute for Digital Convergence-INCoD, Universidade Federal de Santa Catarina, Florianópolis, Santa Catarina, Brazil.
Góes-Neto A; Department of Microbiology, Institute of Biological Sciences, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Minas Gerais, Brazil.
Drechsler-Santos ER; MIND.Funga/MICOLAB, Department of Botany, Universidade Federal de Santa Catarina, Florianópolis, Santa Catarina, Brazil.
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Źródło:
PeerJ [PeerJ] 2024 Jul 09; Vol. 12, pp. e17686. Date of Electronic Publication: 2024 Jul 09 (Print Publication: 2024).
Typ publikacji:
Journal Article
MeSH Terms:
Deep Learning*
Fungi*/classification
Fungi*/isolation & purification
Brazil ; Biodiversity ; Neural Networks, Computer ; Databases, Factual
Czasopismo naukowe
Tytuł:
An interpretable artificial intelligence model based on CT for prognosis of intracerebral hemorrhage: a multicenter study.
Autorzy:
Zhang H; Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, 116000, Liaoning, China.
Yang YF; Laboratory for Medical Imaging Informatics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, 200083, China.; Laboratory for Medical Imaging Informatics, University of Chinese Academy of Sciences, Beijing, 100049, China.
Song XL; Department of Radiology, The Second Affiliated Hospital of Dalian Medical University, Dalian, 116027, Liaoning, China.
Hu HJ; Department of Hemato-oncology, The First Hospital of Changsha, Changsha, 410005, Hunan, China.
Yang YY; Laboratory for Medical Imaging Informatics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, 200083, China.; Laboratory for Medical Imaging Informatics, University of Chinese Academy of Sciences, Beijing, 100049, China.
Zhu X; Department of Gynecology, Hunan Provincial Maternal and Child Health Care Hospital, Changsha, 410028, Hunan, China.
Yang C; Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, 116000, Liaoning, China. .
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Źródło:
BMC medical imaging [BMC Med Imaging] 2024 Jul 09; Vol. 24 (1), pp. 170. Date of Electronic Publication: 2024 Jul 09.
Typ publikacji:
Journal Article; Multicenter Study
MeSH Terms:
Cerebral Hemorrhage*/diagnostic imaging
Tomography, X-Ray Computed*/methods
Deep Learning*
Artificial Intelligence*
Humans ; Prognosis ; Male ; Female ; Retrospective Studies ; Middle Aged ; Aged ; ROC Curve ; Neural Networks, Computer ; Algorithms
Czasopismo naukowe
Tytuł:
Developing a deep learning model for predicting ovarian cancer in Ovarian-Adnexal Reporting and Data System Ultrasound (O-RADS US) Category 4 lesions: A multicenter study.
Autorzy:
Xie W; Department of Ultrasound Medicine, The Second Affiliated Hospital of Fujian medical University, Quanzhou, Fujian Province, 362000, China.; Department of Ultrasound, Fujian Cancer Hospital, Clinical Oncology School of Fujian Medical University, Fuzhou, Fujian Province, 350014, China.
Lin W; Department of Ultrasound Medicine, The Second Affiliated Hospital of Fujian medical University, Quanzhou, Fujian Province, 362000, China.
Li P; Department of Gynecology and Obstetrics, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou, Fujian, 362000, China.
Lai H; Department of Ultrasound, Fujian Provincial Maternity and Children's Hospital, Fuzhou, Fujian Province, 350014, China.
Wang Z; Department of Ultrasound, Nanping First Hospital Affiliated to Fujian Medical University, Nanping, Fujian Province, 35300, China.
Liu P; School of Medicine, Huaqiao University, Quanzhou, Fujian Province, 362000, China.
Huang Y; Department of Ultrasound, Fujian Cancer Hospital, Clinical Oncology School of Fujian Medical University, Fuzhou, Fujian Province, 350014, China.
Liu Y; Quanzhou Bolang Technology Group Co., Ltd, Quanzhou, Fujian Province, 362000, China. .
Tang L; Department of Ultrasound, Fujian Cancer Hospital, Clinical Oncology School of Fujian Medical University, Fuzhou, Fujian Province, 350014, China. .
Lyu G; Department of Ultrasound Medicine, The Second Affiliated Hospital of Fujian medical University, Quanzhou, Fujian Province, 362000, China. lgr_.
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Źródło:
Journal of cancer research and clinical oncology [J Cancer Res Clin Oncol] 2024 Jul 09; Vol. 150 (7), pp. 346. Date of Electronic Publication: 2024 Jul 09.
Typ publikacji:
Journal Article; Multicenter Study
MeSH Terms:
Ovarian Neoplasms*/diagnostic imaging
Ovarian Neoplasms*/pathology
Ovarian Neoplasms*/diagnosis
Deep Learning*
Ultrasonography*/methods
Humans ; Female ; Retrospective Studies ; Middle Aged ; Adult ; Aged ; Young Adult
Czasopismo naukowe
Tytuł:
Integration of wearable devices and deep learning: New possibilities for health management and disease prevention.
Autorzy:
Karako K; Department of Surgery, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
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Źródło:
Bioscience trends [Biosci Trends] 2024 Jul 09; Vol. 18 (3), pp. 201-205. Date of Electronic Publication: 2024 Jun 27.
Typ publikacji:
Editorial
MeSH Terms:
Deep Learning*
Wearable Electronic Devices*
Humans
Opinia redakcyjna
Tytuł:
A comprehensive health assessment approach using ensemble deep learning model for remote patient monitoring with IoT.
Autorzy:
R G; Department of Computer Science and Engineering, SRM Institute of Science and Technology, Vadapalani Campus, Chennai, India.
S M; Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, Tamilnadu, India.
Mathivanan SK; School of Computer Science and Engineering, Galgotias University, Greater Noida, 203201, India.
Shivahare BD; School of Computer Science and Engineering, Galgotias University, Greater Noida, 203201, India.
Chandan RR; Department of Computer Science, School of Management Sciences, Varanasi, Uttar pradesh, 221011, India.
Shah MA; Department of Economics, Kardan University, Parwane Du, 1001, Kabul, Afghanistan. .; Division of Research and Development, Lovely Professional University, Phagwara, Punjab, 144001, India. .; Centre of Research Impact and Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, 140401, India. .
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Źródło:
Scientific reports [Sci Rep] 2024 Jul 08; Vol. 14 (1), pp. 15661. Date of Electronic Publication: 2024 Jul 08.
Typ publikacji:
Journal Article
MeSH Terms:
Deep Learning*
Internet of Things*
Humans ; Monitoring, Physiologic/methods ; Wearable Electronic Devices ; Neural Networks, Computer ; Heart Rate ; Telemedicine ; Remote Sensing Technology/methods
Czasopismo naukowe

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