Computer Assisted Diagnosis

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Main PageComputer Assisted Diagnosis
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 Authored by: Allan Halpern

 Keywords:   computer · machine vision · computer assisted diagnosis
Description This chapter provides a very nice up to date overview as well as the vision on computer assisted diagnosisare systems that assist doctors in the interpretation of medical images. Imaging techniques in X-ray, MRI, and ultrasound diagnostics yield a great deal of information that the radiologist or other medical professional has to analyze and evaluate comprehensively in a short time. CAD systems process digital images for typical appearances and to highlight conspicuous sections, such as possible diseases, in order to offer input to support a decision taken by the professional.
Author(s) Allan Halpern
Owner Allan Halpern→ send e-mail
Status unknown
Status update May 13, 2017
Status by Ralph P. Braun


The early diagnosisis the identification of the nature and cause of a certain phenomenon. Diagnosis is used in many different disciplines with variations in the use of logic, analytics, and experience to determine "cause and effect". In systems engineering and computer science, it is typically used to determine the causes of symptoms, mitigations, and solutions of melanomaThis glossary term has not yet been described. remains challenging.[1] Meta-analysis has demonstrated that the use of dermoscopyDermoscopy is a non invasive diagnostic method. by trained users improves diagnostic accuracy for melanoma compared to naked-eye examination alone, mainly by increasing sensitivity.[2][3][4] As non-physicians detect the majority of melanomas[5] and population-based melanoma screening by clinicians is not currently recommended [6] outside of Germany, there is interest in the development of dermoscopy image analysis algorithms to aid laypersons or non-dermatology physicians in melanoma detection.[7][8][9][10][11]

There have been many efforts to train algorithms to diagnose melanoma.[12][13][14]

A recent landmark paper demonstrates the potential of ‘deep learning’ algorithms to aid in melanoma diagnosis.[15] In their study, the researchers used deep convolutional neural networks (CNNs) which they trained in a large dataset of clinical imagesThis glossary term has not yet been described. and then compared against 21 dermatologists for the binary classificationis a general process related to categorization, the process in which ideas and objects are recognized, differentiated, and understood. of keratinocyte carcinomas versus benignis any condition that is harmless in the long run seborrheic keratosesThis glossary term has not yet been described.; and malignantThis glossary term has not yet been described. melanomas versus benign neviThis glossary term has not yet been described., with the CNN achieving performance on par with all tested experts across both tasks, demonstrating an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists. While the potential for computer assisted diagnosis is great, the lack of a large public dataset of validated skin images has limited the ability to directly compare the diagnostic performance of automated image analysis algorithms across studies and against clinicians. The ISIC ArchiveISIC Archive is the name of the image repository of the International Skin Imaging Collaboration (ISIC). (link available on dermoscopediaDermoscopedia is the name of this website and is providing state of knowledge information concerning dermoscopy - a non invasive diagnostic method. menu bar) was created to address this need and has hosted consecutive international ‘Challenges’ that have engaged the broader computer science community.

There are many potential barriers to the implementation of automated diagnosis in clinical care (e.g., vetting of the accuracy of the algorithms, potential for overdiagnosis and false reassurance, acceptance by the medical community) but it is logical to expect that some form of computer assisted triage or diagnosis is forthcoming in the near future. There are already hundreds of ‘apps’ for mobile devices that are being marketed as educational or assistive devices for melanoma early detection.[16] The medicolegal and social landscape around the adoption of these apps is rapidly changing.

ReferencesThis glossary term has not yet been described.
  1. Marghoob & Scope: The complexity of diagnosing melanoma. J. Invest. Dermatol. 2009;129:11-3. PMID: 19078984. DOI.
  2. Kittler et al.: Diagnostic accuracy of dermoscopy. Lancet Oncol. 2002;3:159-65. PMID: 11902502.
  3. Bafounta et al.: Is dermoscopy (epiluminescence microscopy) useful for the diagnosis of melanoma? Results of a meta-analysis using techniques adapted to the evaluation of diagnostic tests. Arch Dermatol 2001;137:1343-50. PMID: 11594860.
  4. Vestergaard et al.: Dermoscopy compared with naked eye examination for the diagnosis of primary melanoma: a meta-analysis of studies performed in a clinical setting. Br. J. Dermatol. 2008;159:669-76. PMID: 18616769. DOI.
  5. Brady et al.: Patterns of detection in patients with cutaneous melanoma. Cancer 2000;89:342-7. PMID: 10918164.
  6. Bibbins-Domingo et al.: Screening for Skin Cancer: US Preventive Services Task Force Recommendation Statement. JAMA 2016;316:429-35. PMID: 27458948. DOI.
  7. Ferris et al.: Computer-aided classification of melanocytic lesions using dermoscopic images. J. Am. Acad. Dermatol. 2015;73:769-76. PMID: 26386631. DOI.
  8. Zortea et al.: Performance of a dermoscopy-based computer vision system for the diagnosis of pigmented skin lesions compared with visual evaluation by experienced dermatologists. Artif Intell Med 2014;60:13-26. PMID: 24382424. DOI.
  9. Blum et al.: Digital image analysis for diagnosis of cutaneous melanoma. Development of a highly effective computer algorithm based on analysis of 837 melanocytic lesions. Br. J. Dermatol. 2004;151:1029-38. PMID: 15541081. DOI.
  10. Menzies et al.: The performance of SolarScan: an automated dermoscopy image analysis instrument for the diagnosis of primary melanoma. Arch Dermatol 2005;141:1388-96. PMID: 16301386. DOI.
  11. Rubegni et al.: Evaluation of cutaneous melanoma thickness by digital dermoscopy analysis: a retrospective study. Melanoma Res. 2010;20:212-7. PMID: 20375922. DOI.
  12. Masood & Al-Jumaily: Computer aided diagnostic support system for skin cancer: a review of techniques and algorithms. Int J Biomed Imaging 2013;2013:323268. PMID: 24575126. DOI.
  13. Rajpara et al.: Systematic review of dermoscopy and digital dermoscopy/ artificial intelligence for the diagnosis of melanoma. Br. J. Dermatol. 2009;161:591-604. PMID: 19302072. DOI.
  14. Maglogiannis & Doukas: Overview of advanced computer vision systems for skin lesions characterization. IEEE Trans Inf Technol Biomed 2009;13:721-33. PMID: 19304487. DOI.
  15. Esteva et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017;542:115-118. PMID: 28117445. DOI.
  16. March et al.: Practical application of new technologies for melanoma diagnosis: Part I. Noninvasive approaches. J. Am. Acad. Dermatol. 2015;72:929-41; quiz 941-2. PMID: 25980998. DOI.