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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:
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:
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:
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:
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:
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
Tytuł:
Comparing a pre-defined versus deep learning approach for extracting brain atrophy patterns to predict cognitive decline due to Alzheimer's disease in patients with mild cognitive symptoms.
Autorzy:
Arvidsson I; Centre for Mathematical Sciences, Lund University, Lund, Sweden. .
Strandberg O; Clinical Memory Research Unit, Department of Clinical Sciences, Malmö, Lund University, Lund, Sweden.
Palmqvist S; Clinical Memory Research Unit, Department of Clinical Sciences, Malmö, Lund University, Lund, Sweden.; Memory Clinic, Skåne University Hospital, Malmö, Sweden.
Stomrud E; Clinical Memory Research Unit, Department of Clinical Sciences, Malmö, Lund University, Lund, Sweden.; Memory Clinic, Skåne University Hospital, Malmö, Sweden.
Cullen N; Clinical Memory Research Unit, Department of Clinical Sciences, Malmö, Lund University, Lund, Sweden.
Janelidze S; Clinical Memory Research Unit, Department of Clinical Sciences, Malmö, Lund University, Lund, Sweden.
Tideman P; Clinical Memory Research Unit, Department of Clinical Sciences, Malmö, Lund University, Lund, Sweden.; Memory Clinic, Skåne University Hospital, Malmö, Sweden.
Heyden A; Centre for Mathematical Sciences, Lund University, Lund, Sweden.
Åström K; Centre for Mathematical Sciences, Lund University, Lund, Sweden.
Hansson O; Clinical Memory Research Unit, Department of Clinical Sciences, Malmö, Lund University, Lund, Sweden.; Memory Clinic, Skåne University Hospital, Malmö, Sweden.
Mattsson-Carlgren N; Clinical Memory Research Unit, Department of Clinical Sciences, Malmö, Lund University, Lund, Sweden. .; Wallenberg Center for Molecular Medicine, Lund University, Lund, Sweden. .; Department of Neurology, Skåne University Hospital, Lund, Sweden. .
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Źródło:
Alzheimer's research & therapy [Alzheimers Res Ther] 2024 Mar 19; Vol. 16 (1), pp. 61. Date of Electronic Publication: 2024 Mar 19.
Typ publikacji:
Journal Article
MeSH Terms:
Alzheimer Disease*/complications
Alzheimer Disease*/diagnostic imaging
Deep Learning*
Cognitive Dysfunction*/diagnosis
Humans ; Biomarkers ; Magnetic Resonance Imaging ; Brain/diagnostic imaging ; Brain/pathology ; Cognition ; Atrophy/pathology ; Disease Progression
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
Deep-learning-based radiomics of intratumoral and peritumoral MRI images to predict the pathological features of adjuvant radiotherapy in early-stage cervical squamous cell carcinoma.
Autorzy:
Zhang XF; Radiotherapy department, Cancer center, The Tenth Affiliated Hospital, Southern Medical University(Dongguan People's Hospital), No.78 Wandaonan Road, Dongguan, 523059, Guangdong, People's Republic of China.; Dongguan Key Laboratory of Precision Diagnosis and Treatment for Tumors, Dongguan, 523059, Guangdong, People's Republic of China.
Wu HY; Radiotherapy department, Cancer center, The Tenth Affiliated Hospital, Southern Medical University(Dongguan People's Hospital), No.78 Wandaonan Road, Dongguan, 523059, Guangdong, People's Republic of China.; Dongguan Key Laboratory of Precision Diagnosis and Treatment for Tumors, Dongguan, 523059, Guangdong, People's Republic of China.
Liang XW; Radiotherapy department, Cancer center, The Tenth Affiliated Hospital, Southern Medical University(Dongguan People's Hospital), No.78 Wandaonan Road, Dongguan, 523059, Guangdong, People's Republic of China.; Dongguan Key Laboratory of Precision Diagnosis and Treatment for Tumors, Dongguan, 523059, Guangdong, People's Republic of China.
Chen JL; Radiotherapy department, Cancer center, The Tenth Affiliated Hospital, Southern Medical University(Dongguan People's Hospital), No.78 Wandaonan Road, Dongguan, 523059, Guangdong, People's Republic of China.; Dongguan Key Laboratory of Precision Diagnosis and Treatment for Tumors, Dongguan, 523059, Guangdong, People's Republic of China.
Li J; Radiology Department, The Tenth Affiliated Hospital, Southern Medical University(Dongguan People's Hospital), No.78 Wandaonan Road, Dongguan, 523059, Guangdong, People's Republic of China.
Zhang S; Pathology Department, The Tenth Affiliated Hospital, Southern Medical University(Dongguan People's Hospital), No.78 Wandaonan Road, Dongguan, 523059, Guangdong, People's Republic of China.
Liu Z; Radiotherapy department, Cancer center, The Tenth Affiliated Hospital, Southern Medical University(Dongguan People's Hospital), No.78 Wandaonan Road, Dongguan, 523059, Guangdong, People's Republic of China. .; Dongguan Key Laboratory of Precision Diagnosis and Treatment for Tumors, Dongguan, 523059, Guangdong, People's Republic of China. .
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Źródło:
BMC women's health [BMC Womens Health] 2024 Mar 19; Vol. 24 (1), pp. 182. Date of Electronic Publication: 2024 Mar 19.
Typ publikacji:
Journal Article
MeSH Terms:
Carcinoma, Squamous Cell*/diagnostic imaging
Carcinoma, Squamous Cell*/radiotherapy
Deep Learning*
Uterine Cervical Neoplasms*/diagnostic imaging
Uterine Cervical Neoplasms*/radiotherapy
Female ; Humans ; Radiotherapy, Adjuvant ; Radiomics ; Magnetic Resonance Imaging ; Retrospective Studies
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
The development of a prediction model based on deep learning for prognosis prediction of gastrointestinal stromal tumor: a SEER-based study.
Autorzy:
Zeng J; Department of Gastrointestinal Surgery, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei, China.
Li K; Department of Gastrointestinal Surgery, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei, China.
Cao F; Department of Gastrointestinal Surgery, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei, China.
Zheng Y; Department of Gastrointestinal Surgery, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei, China. .
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Źródło:
Scientific reports [Sci Rep] 2024 Mar 19; Vol. 14 (1), pp. 6609. Date of Electronic Publication: 2024 Mar 19.
Typ publikacji:
Journal Article
MeSH Terms:
Gastrointestinal Stromal Tumors*
Deep Learning*
Humans ; Prognosis ; Area Under Curve ; Calibration ; Nomograms ; SEER Program
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
Predictive deep learning models for cognitive risk using accessible data.
Autorzy:
Karako K; Department of Human and Engineered Environmental Studies, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan.
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Źródło:
Bioscience trends [Biosci Trends] 2024 Mar 19; Vol. 18 (1), pp. 66-72. Date of Electronic Publication: 2024 Feb 20.
Typ publikacji:
Journal Article
MeSH Terms:
Dementia*/diagnosis
Deep Learning*
Cognitive Dysfunction*/diagnosis
Cognitive Dysfunction*/psychology
Alzheimer Disease*/diagnosis
Humans ; Cognition ; Neuropsychological Tests ; Disease Progression
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknieSzczegóły
Wyświetlanie 1-20 z 310219

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