Introduction to computer assisted diagnosis

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Main PageComputer Assisted DiagnosisIntroduction to computer assisted diagnosis
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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. in dermoscopyThe examination of [skin lesions] with a 'dermatoscope'. This traditionally consists of a magnifier (typically x10), a non-polarised light source, a transparent plate and a liquid medium between the instrument and the skin, and allows inspection of skin lesions unobstructed by skin surface reflections. Modern dermatoscopes dispense with the use of liquid medium and instead use polarised light to cancel out skin surface reflections. in dermoscopy
Author(s) Allan Halpern
Responsible author Allan Halpern→ send e-mail
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Status update July 9, 2018
Status by Ralph P. Braun


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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 dermoscopy by trained users improves diagnostic accuracyThis glossary term has not yet been described. for melanoma compared to naked-eye examination alone, mainly by increasing sensitivityThis glossary term has not yet been described..[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 learningThis glossary term has not yet been described.’ algorithms to aid in melanoma diagnosis.[15] In their study, the researchers used deep convolutional neural networksThis glossary term has not yet been described. (CNNs) which they trained in a large dataset of clinical imagesA representation of a person, animal or thing, photographed, painted or otherwise made visible. 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 malignant 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 intelligenceThis glossary term has not yet been described. capable of classifying skin cancerThis glossary term has not yet been described. 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 is material contained in a footnote or bibliography holding further information.
  1. Marghoob & Scope: The complexity of diagnosing melanoma. J. Invest. Dermatol. 2009;129:11-3. PMID: 19078984. DOI.
  2. Kittler et al.: Diagnostic accuracyThis glossary term has not yet been described. 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.: DermoscopyThe examination of [skin lesions] with a 'dermatoscope'. This traditionally consists of a magnifier (typically x10), a non-polarised light source, a transparent plate and a liquid medium between the instrument and the skin, and allows inspection of skin lesions unobstructed by skin surface reflections. Modern dermatoscopes dispense with the use of liquid medium and instead use polarised light to cancel out skin surface reflections. 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.: PatternsThis glossary term has not yet been described. 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.: Computeris a device that can be instructed to carry out arbitrary sequences of arithmetic or logical operations automatically. The ability of computers to follow generalized sets of operations, called programs, enables them to perform an extremely wide range of tasks.-aided classification of melanocyticThis glossary term has not yet been described. 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 algorithmIn mathematics and computer science, an algorithm (Listeni/ˈælɡərɪðəm/ AL-gə-ri-dhəm) is a self-contained sequence of actions to be performed. Algorithms can perform calculation, data processing and automated reasoning tasks. 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 dermoscopyDermoscopy using digital images. This is used for telemedicine and monitoring. analysis: a retrospective study. MelanomaThis glossary term has not yet been described. 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 ImagingThis glossary term has not yet been described. 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.