Literature DB >> 30676989

Deep Learning: Current and Emerging Applications in Medicine and Technology.

Altug Akay, Henry Hess.   

Abstract

Machine learning is enabling researchers to analyze and understand increasingly complex physical and biological phenomena in traditional fields such as biology, medicine, and engineering and emerging fields like synthetic biology, automated chemical synthesis, and biomanufacturing. These fields require new paradigms toward understanding increasingly complex data and converting such data into medical products and services for patients. The move toward deep learning and complex modeling is an attempt to bridge the gap between acquiring massive quantities of complex data, and converting such data into practical insights. Here, we provide an overview of the field of machine learning, its current applications and needs in traditional and emerging fields, and discuss an illustrative attempt at using deep learning to understand swarm behavior of molecular shuttles.

Entities:  

Year:  2019        PMID: 30676989     DOI: 10.1109/JBHI.2019.2894713

Source DB:  PubMed          Journal:  IEEE J Biomed Health Inform        ISSN: 2168-2194            Impact factor:   5.772


  9 in total

Review 1.  Rage Against the Machine: Advancing the study of aggression ethology via machine learning.

Authors:  Nastacia L Goodwin; Simon R O Nilsson; Sam A Golden
Journal:  Psychopharmacology (Berl)       Date:  2020-07-09       Impact factor: 4.530

Review 2.  Machine learning-enabled multiplexed microfluidic sensors.

Authors:  Sajjad Rahmani Dabbagh; Fazle Rabbi; Zafer Doğan; Ali Kemal Yetisen; Savas Tasoglu
Journal:  Biomicrofluidics       Date:  2020-12-11       Impact factor: 2.800

Review 3.  Deep learning in generating radiology reports: A survey.

Authors:  Maram Mahmoud A Monshi; Josiah Poon; Vera Chung
Journal:  Artif Intell Med       Date:  2020-05-15       Impact factor: 5.326

4.  A Nomogram Based on CT Deep Learning Signature: A Potential Tool for the Prediction of Overall Survival in Resected Non-Small Cell Lung Cancer Patients.

Authors:  Ting Lin; Jinhai Mai; Meng Yan; Zhenhui Li; Xianyue Quan; Xin Chen
Journal:  Cancer Manag Res       Date:  2021-03-30       Impact factor: 3.989

Review 5.  Deep Learning-Enabled Technologies for Bioimage Analysis.

Authors:  Fazle Rabbi; Sajjad Rahmani Dabbagh; Pelin Angin; Ali Kemal Yetisen; Savas Tasoglu
Journal:  Micromachines (Basel)       Date:  2022-02-06       Impact factor: 2.891

6.  Molecular Insights from Conformational Ensembles via Machine Learning.

Authors:  Oliver Fleetwood; Marina A Kasimova; Annie M Westerlund; Lucie Delemotte
Journal:  Biophys J       Date:  2019-12-21       Impact factor: 4.033

7.  Artificial Intelligence Algorithms to Diagnose Glaucoma and Detect Glaucoma Progression: Translation to Clinical Practice.

Authors:  Anna S Mursch-Edlmayr; Wai Siene Ng; Alberto Diniz-Filho; David C Sousa; Louis Arnold; Matthew B Schlenker; Karla Duenas-Angeles; Pearse A Keane; Jonathan G Crowston; Hari Jayaram
Journal:  Transl Vis Sci Technol       Date:  2020-10-15       Impact factor: 3.283

8.  Automatic Prediction of MGMT Status in Glioblastoma via Deep Learning-Based MR Image Analysis.

Authors:  Xin Chen; Min Zeng; Yichen Tong; Tianjing Zhang; Yan Fu; Haixia Li; Zhongping Zhang; Zixuan Cheng; Xiangdong Xu; Ruimeng Yang; Zaiyi Liu; Xinhua Wei; Xinqing Jiang
Journal:  Biomed Res Int       Date:  2020-09-23       Impact factor: 3.411

9.  In vivo, in vitro and in silico: an open space for the development of microbe-based applications of synthetic biology.

Authors:  Antoine Danchin
Journal:  Microb Biotechnol       Date:  2021-09-27       Impact factor: 5.813

  9 in total

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