Literature DB >> 32852557

Evaluation of the Diagnostic Accuracy of an Online Artificial Intelligence Application for Skin Disease Diagnosis.

Oscar Zaar1, Alexander Larson, Sam Polesie, Karim Saleh, Mikael Tarstedt, Antonio Olives, Andrea Suárez, Martin Gillstedt, Noora Neittaanmäki.   

Abstract

Artificial intelligence (AI) algorithms for automated classification of skin diseases are available to the consumer market. Studies of their diagnostic accuracy are rare. We assessed the diagnostic accuracy of an open-access AI application (Skin Image Search™) for recognition of skin diseases. Clinical images including tumours, infective and inflammatory skin diseases were collected at the Department of Dermatology at the Sahlgrenska University Hospital and uploaded for classification by the online application. The AI algorithm classified the images giving 5 differential diagnoses, which were then compared to the diagnoses made clinically by the dermatologists and/or histologically. We included 521 images portraying 26 diagnoses. The diagnostic accuracy was 56.4% for the top 5 suggested diagnoses and 22.8% when only considering the most probable diagnosis. The level of diagnostic accuracy varied considerably for diagnostic groups. The online application demonstrated low diagnostic accuracy compared to a dermatologist evaluation and needs further development.

Entities:  

Keywords:  dermatology; online diagnostics; skin disease; artificial intelligence

Mesh:

Year:  2020        PMID: 32852557      PMCID: PMC9234984          DOI: 10.2340/00015555-3624

Source DB:  PubMed          Journal:  Acta Derm Venereol        ISSN: 0001-5555            Impact factor:   3.875


  26 in total

1.  Validation of a Melanoma Risk Assessment Smartphone Application.

Authors:  Jemima J Dorairaj; Gerard M Healy; Angela McInerney; Alan J Hussey
Journal:  Dermatol Surg       Date:  2017-02       Impact factor: 3.398

2.  Diagnostic inaccuracy of smartphone applications for melanoma detection.

Authors:  Joel A Wolf; Jacqueline F Moreau; Oleg Akilov; Timothy Patton; Joseph C English; Jonhan Ho; Laura K Ferris
Journal:  JAMA Dermatol       Date:  2013-04       Impact factor: 10.282

3.  Prevalence of skin lesions and need for treatment in a cohort of 90 880 workers.

Authors:  M Augustin; K Herberger; S Hintzen; H Heigel; N Franzke; I Schäfer
Journal:  Br J Dermatol       Date:  2011-10       Impact factor: 9.302

4.  Results of the 2016 International Skin Imaging Collaboration International Symposium on Biomedical Imaging challenge: Comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images.

Authors:  Michael A Marchetti; Noel C F Codella; Stephen W Dusza; David A Gutman; Brian Helba; Aadi Kalloo; Nabin Mishra; Cristina Carrera; M Emre Celebi; Jennifer L DeFazio; Natalia Jaimes; Ashfaq A Marghoob; Elizabeth Quigley; Alon Scope; Oriol Yélamos; Allan C Halpern
Journal:  J Am Acad Dermatol       Date:  2017-09-29       Impact factor: 11.527

5.  Smartphone applications for triaging adults with skin lesions that are suspicious for melanoma.

Authors:  Naomi Chuchu; Yemisi Takwoingi; Jacqueline Dinnes; Rubeta N Matin; Oliver Bassett; Jacqueline F Moreau; Susan E Bayliss; Clare Davenport; Kathie Godfrey; Susan O'Connell; Abhilash Jain; Fiona M Walter; Jonathan J Deeks; Hywel C Williams
Journal:  Cochrane Database Syst Rev       Date:  2018-12-04

6.  Deep neural networks show an equivalent and often superior performance to dermatologists in onychomycosis diagnosis: Automatic construction of onychomycosis datasets by region-based convolutional deep neural network.

Authors:  Seung Seog Han; Gyeong Hun Park; Woohyung Lim; Myoung Shin Kim; Jung Im Na; Ilwoo Park; Sung Eun Chang
Journal:  PLoS One       Date:  2018-01-19       Impact factor: 3.240

7.  Global Skin Disease Morbidity and Mortality: An Update From the Global Burden of Disease Study 2013.

Authors:  Chante Karimkhani; Robert P Dellavalle; Luc E Coffeng; Carsten Flohr; Roderick J Hay; Sinéad M Langan; Elaine O Nsoesie; Alize J Ferrari; Holly E Erskine; Jonathan I Silverberg; Theo Vos; Mohsen Naghavi
Journal:  JAMA Dermatol       Date:  2017-05-01       Impact factor: 10.282

Review 8.  Artificial intelligence in cancer imaging: Clinical challenges and applications.

Authors:  Wenya Linda Bi; Ahmed Hosny; Matthew B Schabath; Maryellen L Giger; Nicolai J Birkbak; Alireza Mehrtash; Tavis Allison; Omar Arnaout; Christopher Abbosh; Ian F Dunn; Raymond H Mak; Rulla M Tamimi; Clare M Tempany; Charles Swanton; Udo Hoffmann; Lawrence H Schwartz; Robert J Gillies; Raymond Y Huang; Hugo J W L Aerts
Journal:  CA Cancer J Clin       Date:  2019-02-05       Impact factor: 508.702

Review 9.  Evidence assessing the diagnostic performance of medical smartphone apps: a systematic review and exploratory meta-analysis.

Authors:  Rahel Buechi; Livia Faes; Lucas M Bachmann; Michael A Thiel; Nicolas S Bodmer; Martin K Schmid; Oliver Job; Kenny R Lienhard
Journal:  BMJ Open       Date:  2017-12-14       Impact factor: 2.692

10.  Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies.

Authors:  Karoline Freeman; Jacqueline Dinnes; Naomi Chuchu; Yemisi Takwoingi; Sue E Bayliss; Rubeta N Matin; Abhilash Jain; Fiona M Walter; Hywel C Williams; Jonathan J Deeks
Journal:  BMJ       Date:  2020-02-10
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  1 in total

1.  Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda.

Authors:  Yogesh Kumar; Apeksha Koul; Ruchi Singla; Muhammad Fazal Ijaz
Journal:  J Ambient Intell Humaniz Comput       Date:  2022-01-13
  1 in total

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