Literature DB >> 30182201

Machine learning: applications of artificial intelligence to imaging and diagnosis.

James A Nichols1, Hsien W Herbert Chan2,3, Matthew A B Baker4.   

Abstract

Machine learning (ML) is a form of artificial intelligence which is placed to transform the twenty-first century. Rapid, recent progress in its underlying architecture and algorithms and growth in the size of datasets have led to increasing computer competence across a range of fields. These include driving a vehicle, language translation, chatbots and beyond human performance at complex board games such as Go. Here, we review the fundamentals and algorithms behind machine learning and highlight specific approaches to learning and optimisation. We then summarise the applications of ML to medicine. In particular, we showcase recent diagnostic performances, and caveats, in the fields of dermatology, radiology, pathology and general microscopy.

Entities:  

Keywords:  Artificial intelligence; Computer vision; Dermatology; Imaging; Machine learning; Microscopy; Radiology

Year:  2018        PMID: 30182201      PMCID: PMC6381354          DOI: 10.1007/s12551-018-0449-9

Source DB:  PubMed          Journal:  Biophys Rev        ISSN: 1867-2450


  26 in total

1.  Machine Learning and Prediction of All-Cause Mortality in COPD.

Authors:  Matthew Moll; Dandi Qiao; Elizabeth A Regan; Gary M Hunninghake; Barry J Make; Ruth Tal-Singer; Michael J McGeachie; Peter J Castaldi; Raul San Jose Estepar; George R Washko; James M Wells; David LaFon; Matthew Strand; Russell P Bowler; MeiLan K Han; Jorgen Vestbo; Bartolome Celli; Peter Calverley; James Crapo; Edwin K Silverman; Brian D Hobbs; Michael H Cho
Journal:  Chest       Date:  2020-04-27       Impact factor: 9.410

2.  ABA/ASB biophysics and medicine session 2018.

Authors:  Matthew A B Baker
Journal:  Biophys Rev       Date:  2019-05-04

3.  Big data: the elements of good questions, open data, and powerful software.

Authors:  Joshua W K Ho; Eleni Giannoulatou
Journal:  Biophys Rev       Date:  2019-01-25

4.  Patient Data-Sharing for AI: Ethical Challenges, Catholic Solutions.

Authors:  Jean Baric-Parker; Emily E Anderson
Journal:  Linacre Q       Date:  2020-05-15

Review 5.  Machine Learning in Neuro-Oncology, Epilepsy, Alzheimer's Disease, and Schizophrenia.

Authors:  Mason English; Chitra Kumar; Bonnie Legg Ditterline; Doniel Drazin; Nicholas Dietz
Journal:  Acta Neurochir Suppl       Date:  2022

Review 6.  Role of Machine Learning and Artificial Intelligence in Interventional Oncology.

Authors:  Brian D'Amore; Sara Smolinski-Zhao; Dania Daye; Raul N Uppot
Journal:  Curr Oncol Rep       Date:  2021-04-20       Impact factor: 5.075

7.  Rapid diagnosis of hereditary haemolytic anaemias using automated rheoscopy and supervised machine learning.

Authors:  Pedro L Moura; Johannes G G Dobbe; Geert J Streekstra; Minke A E Rab; Martijn Veldthuis; Elisa Fermo; Richard van Wijk; Rob van Zwieten; Paola Bianchi; Ashley M Toye; Timothy J Satchwell
Journal:  Br J Haematol       Date:  2020-07-05       Impact factor: 6.998

Review 8.  Deep Learning in Biomedical Optics.

Authors:  Lei Tian; Brady Hunt; Muyinatu A Lediju Bell; Ji Yi; Jason T Smith; Marien Ochoa; Xavier Intes; Nicholas J Durr
Journal:  Lasers Surg Med       Date:  2021-05-20

9.  AutoSmarTrace: Automated chain tracing and flexibility analysis of biological filaments.

Authors:  Mathew Schneider; Alaa Al-Shaer; Nancy R Forde
Journal:  Biophys J       Date:  2021-05-20       Impact factor: 3.699

10.  Machine learning reveals the most important psychological and social variables predicting the differential diagnosis of rheumatic and musculoskeletal diseases.

Authors:  Germano Vera Cruz; Emilie Bucourt; Christian Réveillère; Virginie Martaillé; Isabelle Joncker-Vannier; Philippe Goupille; Denis Mulleman; Robert Courtois
Journal:  Rheumatol Int       Date:  2021-06-14       Impact factor: 2.631

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