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Tytuł:
Accuracy of generative deep learning model for macular anatomy prediction from optical coherence tomography images in macular hole surgery.
Autorzy:
Kwon HJ; Department of Ophthalmology, Biomedical Research Institute, Pusan National University Hospital, Pusan National University School of Medicine, Gudeok-ro 179, Seo-gu, Busan, 49241, South Korea.
Heo J; Department of Ophthalmology, Biomedical Research Institute, Pusan National University Hospital, Pusan National University School of Medicine, Gudeok-ro 179, Seo-gu, Busan, 49241, South Korea.
Park SH; Department of Ophthalmology, Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Geumo-ro 20, Mulgeum-eup, Yangsan-si, Gyeongsangnam-do, 50612, South Korea.
Park SW; Department of Ophthalmology, Biomedical Research Institute, Pusan National University Hospital, Pusan National University School of Medicine, Gudeok-ro 179, Seo-gu, Busan, 49241, South Korea.
Byon I; Department of Ophthalmology, Biomedical Research Institute, Pusan National University Hospital, Pusan National University School of Medicine, Gudeok-ro 179, Seo-gu, Busan, 49241, South Korea. .
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Źródło:
Scientific reports [Sci Rep] 2024 Mar 22; Vol. 14 (1), pp. 6913. Date of Electronic Publication: 2024 Mar 22.
Typ publikacji:
Journal Article
MeSH Terms:
Retinal Perforations*/diagnostic imaging
Retinal Perforations*/surgery
Deep Learning*
Humans ; Tomography, Optical Coherence/methods ; Artificial Intelligence ; Retina ; Retrospective Studies
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
Drug-Online: an online platform for drug-target interaction, affinity, and binding sites identification using deep learning.
Autorzy:
Zeng X; College of Mathematics and Computer Science, Dali University, Dali, 671003, China.
Su GP; College of Mathematics and Computer Science, Dali University, Dali, 671003, China.
Li SJ; Yunnan Institute of Endemic Diseases Control and Prevention, Dali, 671000, China.
Lv SQ; Institute of Surveying and Information Engineering West, Yunnan University of Applied Science, Dali, 671000, China.
Wen ML; State Key Laboratory for Conservation and Utilization of Bio-Resources in Yunnan, Yunnan University, Kunming, 650000, China.
Li Y; College of Mathematics and Computer Science, Dali University, Dali, 671003, China. .
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Źródło:
BMC bioinformatics [BMC Bioinformatics] 2024 Apr 20; Vol. 25 (1), pp. 156. Date of Electronic Publication: 2024 Apr 20.
Typ publikacji:
Journal Article
MeSH Terms:
Deep Learning*
Drug Interactions ; Binding Sites ; Drug Delivery Systems ; Drug Evaluation, Preclinical
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
Development and application of a deep learning-based comprehensive early diagnostic model for chronic obstructive pulmonary disease.
Autorzy:
Zhu Z; Center of Clinical Big Data and Analytics of The Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Zhao S; Department of Respiratory and Critical Care Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Lanxi Branch (Lanxi People's Hospital), Hangzhou, Zhejiang, China.
Li J; Center of Clinical Big Data and Analytics of The Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Wang Y; Center of Clinical Big Data and Analytics of The Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Xu L; Center of Clinical Big Data and Analytics of The Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Jia Y; Center of Clinical Big Data and Analytics of The Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Li Z; Center of Clinical Big Data and Analytics of The Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Li W; Center of Clinical Big Data and Analytics of The Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Chen G; College of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang, China. .
Wu X; Center of Clinical Big Data and Analytics of The Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China. .; National Institute for Data Science in Health and Medicine, Zhejiang University, Hangzhou, Zhejiang, China. .; The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, Zhejiang, China. .
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Źródło:
Respiratory research [Respir Res] 2024 Apr 18; Vol. 25 (1), pp. 167. Date of Electronic Publication: 2024 Apr 18.
Typ publikacji:
Journal Article
MeSH Terms:
Deep Learning*
Pulmonary Disease, Chronic Obstructive*/diagnostic imaging
Pulmonary Disease, Chronic Obstructive*/epidemiology
Humans ; Area Under Curve ; Neural Networks, Computer ; ROC Curve ; Retrospective Studies
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
Clinical feasibility of deep learning-based synthetic CT images from T2-weighted MR images for cervical cancer patients compared to MRCAT.
Autorzy:
Kim H; Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, 50-1 Yonsei-Ro, Seodaemun-gu, Seoul, 03722, Korea.
Yoo SK; Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, 50-1 Yonsei-Ro, Seodaemun-gu, Seoul, 03722, Korea.
Kim JS; Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, 50-1 Yonsei-Ro, Seodaemun-gu, Seoul, 03722, Korea.
Kim YT; Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, 50-1 Yonsei-Ro, Seodaemun-gu, Seoul, 03722, Korea.
Lee JW; Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, 50-1 Yonsei-Ro, Seodaemun-gu, Seoul, 03722, Korea.
Kim C; Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, 50-1 Yonsei-Ro, Seodaemun-gu, Seoul, 03722, Korea.
Hong CS; Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, 50-1 Yonsei-Ro, Seodaemun-gu, Seoul, 03722, Korea.
Lee H; Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, 50-1 Yonsei-Ro, Seodaemun-gu, Seoul, 03722, Korea.
Han MC; Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, 50-1 Yonsei-Ro, Seodaemun-gu, Seoul, 03722, Korea.
Kim DW; Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, 50-1 Yonsei-Ro, Seodaemun-gu, Seoul, 03722, Korea.
Kim SY; Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, 50-1 Yonsei-Ro, Seodaemun-gu, Seoul, 03722, Korea.
Kim TM; Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, 50-1 Yonsei-Ro, Seodaemun-gu, Seoul, 03722, Korea.
Kim WH; Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, 50-1 Yonsei-Ro, Seodaemun-gu, Seoul, 03722, Korea.
Kong J; Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, 50-1 Yonsei-Ro, Seodaemun-gu, Seoul, 03722, Korea.
Kim YB; Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, 50-1 Yonsei-Ro, Seodaemun-gu, Seoul, 03722, Korea. .
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Źródło:
Scientific reports [Sci Rep] 2024 Apr 12; Vol. 14 (1), pp. 8504. Date of Electronic Publication: 2024 Apr 12.
Typ publikacji:
Journal Article
MeSH Terms:
Uterine Cervical Neoplasms*/diagnostic imaging
Deep Learning*
Female ; Humans ; Feasibility Studies ; Image Processing, Computer-Assisted/methods ; Magnetic Resonance Imaging/methods ; Tomography, X-Ray Computed/methods ; Radiotherapy Planning, Computer-Assisted/methods
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
HARNet in deep learning approach-a systematic survey.
Autorzy:
Kumar NS; Department of Computer Science and Engineering, SRM Institute of Science and Technology, Vadapalani, Chennai, Tamil Nadu, 600026, India.
Deepika G; Department of Electronics and Communication Engineering, St. Peter's Engineering College, Dhulapally, Hyderabad, 500100, India.
Goutham V; Department of Computer Science and Engineering, St Mary's Group of Institutions, Hyderabad, 500100, India.
Buvaneswari B; Department of Information Technology, Panimalar Engineering College, Poonamallee, Chennai, Tamil Nadu, 600123, India.
Reddy RVK; Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, 522502, India.
Angadi S; Department of Computer Science and Engineering, Nutan College of Engineering and Research, Talegaon Dabhade, Pune, 410507, India.
Dhanamjayulu C; School of Electrical Engineering, Vellore Institute of Technology, Vellore, India. .
Chinthaginjala R; School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
Mohammad F; Department of Chemistry, College of Science, King Saud University, P.O. Box 2455, 11451, Riyadh, Kingdom of Saudi Arabia.
Khan B; Department of Electrical and Computer Engineering, Hawassa University, Hawassa 05, Ethiopia. .
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Źródło:
Scientific reports [Sci Rep] 2024 Apr 10; Vol. 14 (1), pp. 8363. Date of Electronic Publication: 2024 Apr 10.
Typ publikacji:
Journal Article
MeSH Terms:
Deep Learning*
Humans ; Neural Networks, Computer ; Human Activities
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
Automatic measurement of lower limb alignment in portable devices based on deep learning for knee osteoarthritis.
Autorzy:
Yang J; Department of Orthopedics, the First Medical Center of Chinese PLA General Hospital, Beijing, China.; Senior Department of Orthopedics, the Fourth Medical Center of Chinese PLA General Hospital, Beijing, China.
Ren P; Senior Department of Orthopedics, the Fourth Medical Center of Chinese PLA General Hospital, Beijing, China.
Xin P; Department of Orthopedics, Chinese PLA Southern Theater Command General Hospital, Guangzhou, China.
Wang Y; Department of Orthopedics, the First Medical Center of Chinese PLA General Hospital, Beijing, China.; Senior Department of Orthopedics, the Fourth Medical Center of Chinese PLA General Hospital, Beijing, China.; Medical School of Chinese People's Liberation Army, Beijing, China.
Ma Y; Department of Anesthesiology, Guangzhou First People's Hospital, Guangzhou, China.
Liu W; Damo Academy, Alibaba Group, Hangzhou, China.
Wang Y; Damo Academy, Alibaba Group, Hangzhou, China.
Wang Y; Department of Orthopedics, the First Medical Center of Chinese PLA General Hospital, Beijing, China. yanwang_.; Senior Department of Orthopedics, the Fourth Medical Center of Chinese PLA General Hospital, Beijing, China. yanwang_.; Department of Orthopedics, the First Medical Center, PLA General Hospital, Fuxing Road, Haidian District, Beijing, China. yanwang_.
Zhang G; Department of Orthopedics, the First Medical Center of Chinese PLA General Hospital, Beijing, China. gqzhang_.; Senior Department of Orthopedics, the Fourth Medical Center of Chinese PLA General Hospital, Beijing, China. gqzhang_.; Department of Orthopedics, the First Medical Center, PLA General Hospital, Fuxing Road, Haidian District, Beijing, China. gqzhang_.; Department of Orthopedic Surgery, The First Medical Center, Chinese PLA General Hospital, 28 Fuxing Road, Beijing, People's Republic of China. gqzhang_.
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Źródło:
Journal of orthopaedic surgery and research [J Orthop Surg Res] 2024 Apr 10; Vol. 19 (1), pp. 232. Date of Electronic Publication: 2024 Apr 10.
Typ publikacji:
Journal Article
MeSH Terms:
Osteoarthritis, Knee*/diagnostic imaging
Deep Learning*
Humans ; Reproducibility of Results ; Lower Extremity/diagnostic imaging ; Knee Joint/diagnostic imaging ; Tibia ; Femur ; Retrospective Studies
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
Boosting Clear Cell Renal Carcinoma-Specific Drug Discovery Using a Deep Learning Algorithm and Single-Cell Analysis.
Autorzy:
Wang Y; School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China.
Chen X; School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China.
Tang N; School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China.
Guo M; School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China.
Ai D; School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China.
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Źródło:
International journal of molecular sciences [Int J Mol Sci] 2024 Apr 08; Vol. 25 (7). Date of Electronic Publication: 2024 Apr 08.
Typ publikacji:
Journal Article
MeSH Terms:
Carcinoma, Renal Cell*/drug therapy
Deep Learning*
Kidney Neoplasms*/drug therapy
Humans ; Endothelial Cells ; Algorithms ; Single-Cell Analysis ; Antimetabolites ; DNA Modification Methylases ; Drug Discovery ; DNA ; Tumor Microenvironment
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
Exploring Deep Learning to Predict Coconut Milk Adulteration Using FT-NIR and Micro-NIR Spectroscopy.
Autorzy:
Sitorus A; Department of Agricultural Engineering, School of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok 10520, Thailand.
Lapcharoensuk R; Department of Agricultural Engineering, School of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok 10520, Thailand.
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Źródło:
Sensors (Basel, Switzerland) [Sensors (Basel)] 2024 Apr 08; Vol. 24 (7). Date of Electronic Publication: 2024 Apr 08.
Typ publikacji:
Journal Article
MeSH Terms:
Cocos*
Deep Learning*
Animals ; Milk ; Spectroscopy, Near-Infrared ; Starch
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
Brain-machine interface based on deep learning to control asynchronously a lower-limb robotic exoskeleton: a case-of-study.
Autorzy:
Ferrero L; Brain-Machine Interface Systems Lab, Miguel Hernández University of Elche, Elche, Spain. .; Instituto de Investigación en Ingeniería de Elche-I3E, Miguel Hernández University of Elche, Elche, Spain. .; International Affiliate NSF IUCRC BRAIN Site, Miguel Hernández University of Elche, Elche, Spain. .; NSF IUCRC BRAIN, University of Houston, Houston, USA. .; Non-Invasive Brain Machine Interface Systems, University of Houston, Houston, TX, USA. .
Soriano-Segura P; Brain-Machine Interface Systems Lab, Miguel Hernández University of Elche, Elche, Spain.; Instituto de Investigación en Ingeniería de Elche-I3E, Miguel Hernández University of Elche, Elche, Spain.; International Affiliate NSF IUCRC BRAIN Site, Miguel Hernández University of Elche, Elche, Spain.
Navarro J; NSF IUCRC BRAIN, University of Houston, Houston, USA.; International Affiliate NSF IUCRC BRAIN Site, Tecnológico de Monterrey, Monterrey, Mexico.; Non-Invasive Brain Machine Interface Systems, University of Houston, Houston, TX, USA.
Jones O; NSF IUCRC BRAIN, University of Houston, Houston, USA.; Non-Invasive Brain Machine Interface Systems, University of Houston, Houston, TX, USA.
Ortiz M; Brain-Machine Interface Systems Lab, Miguel Hernández University of Elche, Elche, Spain.; Instituto de Investigación en Ingeniería de Elche-I3E, Miguel Hernández University of Elche, Elche, Spain.; International Affiliate NSF IUCRC BRAIN Site, Miguel Hernández University of Elche, Elche, Spain.
Iáñez E; Brain-Machine Interface Systems Lab, Miguel Hernández University of Elche, Elche, Spain.; Instituto de Investigación en Ingeniería de Elche-I3E, Miguel Hernández University of Elche, Elche, Spain.; International Affiliate NSF IUCRC BRAIN Site, Miguel Hernández University of Elche, Elche, Spain.
Azorín JM; Brain-Machine Interface Systems Lab, Miguel Hernández University of Elche, Elche, Spain.; Instituto de Investigación en Ingeniería de Elche-I3E, Miguel Hernández University of Elche, Elche, Spain.; International Affiliate NSF IUCRC BRAIN Site, Miguel Hernández University of Elche, Elche, Spain.; Valencian Graduate School and Research Network of Artificial Intelligence-valgrAI, Valencia, Spain.
Contreras-Vidal JL; NSF IUCRC BRAIN, University of Houston, Houston, USA.; Non-Invasive Brain Machine Interface Systems, University of Houston, Houston, TX, USA.
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Źródło:
Journal of neuroengineering and rehabilitation [J Neuroeng Rehabil] 2024 Apr 05; Vol. 21 (1), pp. 48. Date of Electronic Publication: 2024 Apr 05.
Typ publikacji:
Journal Article; Research Support, U.S. Gov't, Non-P.H.S.; Research Support, Non-U.S. Gov't
MeSH Terms:
Deep Learning*
Exoskeleton Device*
Brain-Computer Interfaces*
Humans ; Algorithms ; Lower Extremity ; Electroencephalography/methods
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
AcquisitionFocus: Joint Optimization of Acquisition Orientation and Cardiac Volume Reconstruction Using Deep Learning.
Autorzy:
Weihsbach C; Institute of Medical Informatics, University of Lübeck, 23562 Lübeck, Germany.
Vogt N; IADI U1254, Inserm, Université de Lorraine, 54511 Nancy, France.
Al-Haj Hemidi Z; Institute of Medical Informatics, University of Lübeck, 23562 Lübeck, Germany.
Bigalke A; Institute of Medical Informatics, University of Lübeck, 23562 Lübeck, Germany.
Hansen L; EchoScout GmbH, 23562 Lübeck, Germany.
Oster J; IADI U1254, Inserm, Université de Lorraine, 54511 Nancy, France.; CHRU-Nancy, Inserm, Université de Lorraine, CIC 1433, Innovation Technologique, 54000 Nancy, France.
Heinrich MP; Institute of Medical Informatics, University of Lübeck, 23562 Lübeck, Germany.
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Źródło:
Sensors (Basel, Switzerland) [Sensors (Basel)] 2024 Apr 04; Vol. 24 (7). Date of Electronic Publication: 2024 Apr 04.
Typ publikacji:
Journal Article
MeSH Terms:
Deep Learning*
Cardiac Surgical Procedures*
Cardiac Volume ; Heart/diagnostic imaging ; Artifacts
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
A deep learning framework for identifying and segmenting three vessels in fetal heart ultrasound images.
Autorzy:
Yan L; College of Information Engineering, Zhejiang University of Technology, Hangzhou, Zhejiang, China.; Hangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou, Zhejiang, China.
Ling S; Hangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou, Zhejiang, China.
Mao R; College of Information Engineering, Zhejiang University of Technology, Hangzhou, Zhejiang, China.; Hangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou, Zhejiang, China.
Xi H; Hangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou, Zhejiang, China.
Wang F; The Center of Four-Dimensional Ultrasound, Affiliated Xiaoshan Hospital, Hangzhou Normal University, Hangzhou, Zhejiang, China. .
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Źródło:
Biomedical engineering online [Biomed Eng Online] 2024 Apr 02; Vol. 23 (1), pp. 39. Date of Electronic Publication: 2024 Apr 02.
Typ publikacji:
Journal Article
MeSH Terms:
Deep Learning*
Pregnancy ; Female ; Humans ; Vena Cava, Superior ; Ultrasonography ; Ultrasonography, Prenatal/methods ; Fetal Heart/diagnostic imaging ; Image Processing, Computer-Assisted/methods
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
A Novel Framework Based on Deep Learning Architecture for Continuous Human Activity Recognition with Inertial Sensors.
Autorzy:
Suglia V; Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, 70126 Bari, Italy.
Palazzo L; Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, 70126 Bari, Italy.; Scientific Clinical Institutes Maugeri SPA SB IRCCS, 70124 Bari, Italy.
Bevilacqua V; Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, 70126 Bari, Italy.; Apulian Bioengineering S.R.L.,Via delle Violette 14, 70026 Modugno, Italy.
Passantino A; Scientific Clinical Institutes Maugeri SPA SB IRCCS, 70124 Bari, Italy.
Pagano G; Scientific Clinical Institutes Maugeri SPA SB IRCCS, 70124 Bari, Italy.
D'Addio G; Scientific Clinical Institutes Maugeri SPA SB IRCCS, 70124 Bari, Italy.
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Źródło:
Sensors (Basel, Switzerland) [Sensors (Basel)] 2024 Mar 29; Vol. 24 (7). Date of Electronic Publication: 2024 Mar 29.
Typ publikacji:
Journal Article
MeSH Terms:
Deep Learning*
Humans ; Human Activities ; Activities of Daily Living ; Engineering ; Healthy Volunteers
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
A Comparison Study of Deep Learning Methodologies for Music Emotion Recognition.
Autorzy:
Louro PL; CISUC, LASI, DEI, FCTUC, University of Coimbra, 3030-790 Coimbra, Portugal.
Redinho H; CISUC, LASI, DEI, FCTUC, University of Coimbra, 3030-790 Coimbra, Portugal.
Malheiro R; CISUC, LASI, DEI, FCTUC, University of Coimbra, 3030-790 Coimbra, Portugal.; School of Technology and Management, Polytechnic Institute of Leiria, 2411-901 Leiria, Portugal.
Paiva RP; CISUC, LASI, DEI, FCTUC, University of Coimbra, 3030-790 Coimbra, Portugal.
Panda R; CISUC, LASI, DEI, FCTUC, University of Coimbra, 3030-790 Coimbra, Portugal.; Ci2-Smart Cities Research Center, Polytechnic Institute of Tomar, 2300-313 Tomar, Portugal.
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Źródło:
Sensors (Basel, Switzerland) [Sensors (Basel)] 2024 Mar 29; Vol. 24 (7). Date of Electronic Publication: 2024 Mar 29.
Typ publikacji:
Journal Article
MeSH Terms:
Deep Learning*
Music*
Data Accuracy ; Emotions ; Machine Learning
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
GraphKM: machine and deep learning for K M prediction of wildtype and mutant enzymes.
Autorzy:
He X; College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Nanjing, China.
Yan M; College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Nanjing, China. .
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Źródło:
BMC bioinformatics [BMC Bioinformatics] 2024 Mar 28; Vol. 25 (1), pp. 135. Date of Electronic Publication: 2024 Mar 28.
Typ publikacji:
Journal Article
MeSH Terms:
Deep Learning*
Electric Power Supplies ; Language ; Neural Networks, Computer ; Protein Engineering
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
Deep learning model for classifying shoulder pain rehabilitation exercises using IMU sensor.
Autorzy:
Lee K; Dept. of Rehabilitation Medicine, Seoul Metropolitan Government Boramae Medical Center, Seoul, South Korea.
Kim JH; Dept. of Rehabilitation Medicine, Seoul Metropolitan Government Boramae Medical Center, Seoul, South Korea.
Hong H; Dept. of Rehabilitation Medicine, Seoul Metropolitan Government Boramae Medical Center, Seoul, South Korea.
Jeong Y; Dept. of Rehabilitation Medicine, Seoul Metropolitan Government Boramae Medical Center, Seoul, South Korea.
Ryu H; Dept. of Graduate School of Technology and Innovation Management, Hanyang University, Seoul, South Korea.
Kim H; Dept. of Intelligence Computing, Hanyang University, Seoul, South Korea.
Lee SU; Dept. of Rehabilitation Medicine, Seoul Metropolitan Government Boramae Medical Center, Seoul, South Korea. .; Dept. of Physical Medicine & Rehabilitation, College of Medicine, Seoul National University, Seoul, South Korea. .
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Źródło:
Journal of neuroengineering and rehabilitation [J Neuroeng Rehabil] 2024 Mar 27; Vol. 21 (1), pp. 42. Date of Electronic Publication: 2024 Mar 27.
Typ publikacji:
Journal Article; Research Support, Non-U.S. Gov't
MeSH Terms:
Shoulder Pain*/diagnosis
Deep Learning*
Male ; Female ; Humans ; Adult ; Middle Aged ; Aged ; Aged, 80 and over ; Artificial Intelligence ; Exercise Therapy ; Shoulder
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
Automated analysis of knee joint alignment using detailed angular values in long leg radiographs based on deep learning.
Autorzy:
Lee HS; Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, 211, Eonju-ro, Gangnam-gu, Seoul, Republic of Korea.
Hwang S; Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Republic of Korea.
Kim SH; Department of Orthopedic Surgery, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea.
Joon NB; Department of Orthopedic Surgery, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea.
Kim H; Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, 211, Eonju-ro, Gangnam-gu, Seoul, Republic of Korea.
Hong YS; Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, 211, Eonju-ro, Gangnam-gu, Seoul, Republic of Korea.
Kim S; Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, 211, Eonju-ro, Gangnam-gu, Seoul, Republic of Korea. .
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Źródło:
Scientific reports [Sci Rep] 2024 Mar 27; Vol. 14 (1), pp. 7226. Date of Electronic Publication: 2024 Mar 27.
Typ publikacji:
Journal Article
MeSH Terms:
Deep Learning*
Osteoarthritis, Knee*/etiology
Humans ; Leg ; Retrospective Studies ; Knee Joint/diagnostic imaging ; Tibia
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
Utilizing genomic signatures to gain insights into the dynamics of SARS-CoV-2 through Machine and Deep Learning techniques.
Autorzy:
Elsherbini AMA; Bioinformatics Group, Center for Informatics Science, School of Information Technology and Computer Science, Nile University, Giza, Egypt.
Elkholy AH; Bioinformatics Group, Center for Informatics Science, School of Information Technology and Computer Science, Nile University, Giza, Egypt.
Fadel YM; Bioinformatics Group, Center for Informatics Science, School of Information Technology and Computer Science, Nile University, Giza, Egypt.
Goussarov G; Microbiology Unit, Belgian Nuclear Research Centre (SCK•CEN), Mol, Belgium.
Elshal AM; Bioinformatics Group, Center for Informatics Science, School of Information Technology and Computer Science, Nile University, Giza, Egypt.
El-Hadidi M; Bioinformatics Group, Center for Informatics Science, School of Information Technology and Computer Science, Nile University, Giza, Egypt.
Mysara M; Bioinformatics Group, Center for Informatics Science, School of Information Technology and Computer Science, Nile University, Giza, Egypt. .
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Źródło:
BMC bioinformatics [BMC Bioinformatics] 2024 Mar 27; Vol. 25 (1), pp. 131. Date of Electronic Publication: 2024 Mar 27.
Typ publikacji:
Journal Article
MeSH Terms:
Deep Learning*
COVID-19*/epidemiology
Humans ; SARS-CoV-2/genetics ; Phylogeny ; Genomics ; Nucleotides
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
Deep learning in cancer genomics and histopathology.
Autorzy:
Unger M; Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany. .
Kather JN; Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany. jakob_.; Department of Medicine I, University Hospital Dresden, Dresden, Germany. jakob_.; Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany. jakob_.
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Źródło:
Genome medicine [Genome Med] 2024 Mar 27; Vol. 16 (1), pp. 44. Date of Electronic Publication: 2024 Mar 27.
Typ publikacji:
Journal Article; Review
MeSH Terms:
Neoplasms*/genetics
Neoplasms*/diagnosis
Deep Learning*
Humans ; Artificial Intelligence ; Precision Medicine/methods ; Genomics/methods
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
GeneAI 3.0: powerful, novel, generalized hybrid and ensemble deep learning frameworks for miRNA species classification of stationary patterns from nucleotides.
Autorzy:
Singh J; Department of Computer Science, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India.
Khanna NN; Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi, India.
Rout RK; Department of Computer Science and Engineering, NIT Srinagar, Hazratbal, Srinagar, India.
Singh N; Department of Food Science, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India.
Laird JR; Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA, USA.
Singh IM; Advanced Cardiac and Vascular Institute, Sacramento, CA, USA.
Kalra MK; Department of Radiology, Massachusetts General Hospital, Boston, MA, 02115, USA.
Mantella LE; Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada.
Johri AM; Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada.
Isenovic ER; Laboratory for Molecular Genetics and Radiobiology, University of Belgrade, Belgrade, Serbia.
Fouda MM; Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID, 83209, USA.
Saba L; Department of Neurology, University of Cagliari, Cagliari, Italy.
Fatemi M; Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, 55905, USA.
Suri JS; Stroke Monitoring and Diagnostic Division, AtheroPoint LLC, Roseville, CA, 95661, USA. .
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Źródło:
Scientific reports [Sci Rep] 2024 Mar 26; Vol. 14 (1), pp. 7154. Date of Electronic Publication: 2024 Mar 26.
Typ publikacji:
Journal Article
MeSH Terms:
MicroRNAs*
Deep Learning*
Humans ; Animals ; Mice ; Rats ; Nucleotides ; Reproducibility of Results ; Area Under Curve
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
Landet: an efficient physics-informed deep learning approach for automatic detection of anatomical landmarks and measurement of spinopelvic alignment.
Autorzy:
MohammadiNasrabadi A; Department of Systems Design Engineering, University of Waterloo, 200 University Ave W, Waterloo, ON, N2L 3G1, Canada. .
Moammer G; Department of Spine Surgery, Grand River Hospital (GRH), 835 King St W, Kitchener, ON, N2G 1G3, Canada.
Quateen A; Department of Spine Surgery, Grand River Hospital (GRH), 835 King St W, Kitchener, ON, N2G 1G3, Canada.
Bhanot K; Department of Surgery, McMaster University, 1280 Main Street West, Hamilton, ON, L8S 4K1, Canada.
McPhee J; Department of Systems Design Engineering, University of Waterloo, 200 University Ave W, Waterloo, ON, N2L 3G1, Canada.
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Źródło:
Journal of orthopaedic surgery and research [J Orthop Surg Res] 2024 Mar 25; Vol. 19 (1), pp. 199. Date of Electronic Publication: 2024 Mar 25.
Typ publikacji:
Journal Article
MeSH Terms:
Deep Learning*
Lordosis*
Humans ; Reproducibility of Results ; Sacrum/diagnostic imaging ; Pelvis/diagnostic imaging ; Lumbar Vertebrae/surgery
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
Integrated image and location analysis for wound classification: a deep learning approach.
Autorzy:
Patel Y; Department of Computer Science, University of Wisconsin-Milwaukee, Milwaukee, WI, USA.
Shah T; Department of Computer Science, University of Wisconsin-Milwaukee, Milwaukee, WI, USA.
Dhar MK; Department of Computer Science, University of Wisconsin-Milwaukee, Milwaukee, WI, USA.
Zhang T; Department of Computer Science, University of Wisconsin-Milwaukee, Milwaukee, WI, USA.
Niezgoda J; Advancing the Zenith of Healthcare (AZH) Wound and Vascular Center, Milwaukee, WI, USA.
Gopalakrishnan S; College of Nursing, University of Wisconsin Milwaukee, Milwaukee, WI, USA.
Yu Z; Department of Computer Science, University of Wisconsin-Milwaukee, Milwaukee, WI, USA. .; Department of Biomedical Engineering, University of Wisconsin-Milwaukee, Milwaukee, WI, USA. .
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Źródło:
Scientific reports [Sci Rep] 2024 Mar 25; Vol. 14 (1), pp. 7043. Date of Electronic Publication: 2024 Mar 25.
Typ publikacji:
Journal Article
MeSH Terms:
Deep Learning*
Accidental Injuries*
Neoplasms, Squamous Cell*
Humans ; Benchmarking ; Neural Networks, Computer
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
Amount Estimation Method for Food Intake Based on Color and Depth Images through Deep Learning.
Autorzy:
Lee DS; AI Grand ICT Center, Dong-Eui University, Busan 47340, Republic of Korea.
Kwon SK; Department of Computer Software Engineering, Dong-Eui University, Busan 47340, Republic of Korea.
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Źródło:
Sensors (Basel, Switzerland) [Sensors (Basel)] 2024 Mar 22; Vol. 24 (7). Date of Electronic Publication: 2024 Mar 22.
Typ publikacji:
Journal Article
MeSH Terms:
Deep Learning*
Computer Simulation ; Food ; Postprandial Period ; Eating
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
Heart Rate Variability Monitoring Based on Doppler Radar Using Deep Learning.
Autorzy:
Yuan S; School of Electronics and Communication Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China.
Fan S; School of Electronics and Communication Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China.
Deng Z; School of Electronics and Communication Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China.
Pan P; School of Electronics and Communication Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China.
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Źródło:
Sensors (Basel, Switzerland) [Sensors (Basel)] 2024 Mar 22; Vol. 24 (7). Date of Electronic Publication: 2024 Mar 22.
Typ publikacji:
Journal Article
MeSH Terms:
Deep Learning*
Heart Rate ; Radar ; Heart Rate Determination ; Algorithms
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
Multimodal deep learning-based diagnostic model for BPPV.
Autorzy:
Lu H; State Key Laboratory of Power Transmission Equipment Technology, School of Electrical Engineering, Chongqing University, Chongqing, China.
Mao Y; State Key Laboratory of Power Transmission Equipment Technology, School of Electrical Engineering, Chongqing University, Chongqing, China. .
Li J; State Key Laboratory of Power Transmission Equipment Technology, School of Electrical Engineering, Chongqing University, Chongqing, China.
Zhu L; State Key Laboratory of Power Transmission Equipment Technology, School of Electrical Engineering, Chongqing University, Chongqing, China.
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Źródło:
BMC medical informatics and decision making [BMC Med Inform Decis Mak] 2024 Mar 21; Vol. 24 (1), pp. 82. Date of Electronic Publication: 2024 Mar 21.
Typ publikacji:
Journal Article
MeSH Terms:
Deep Learning*
Nystagmus, Pathologic*/diagnosis
Humans ; Benign Paroxysmal Positional Vertigo/diagnosis ; Artificial Intelligence ; Hospitals
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
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