Past and present of computer-assisted dermoscopic diagnosis: performance of a conventional image analyser versus a convolutional neural network in a prospective data set of 1,981 skin lesions.
Convolutional neural networks (CNNs) have shown a dermatologist-level performance in the classification of skin lesions. We aimed to deliver a head-to-head comparison of a conventional image analyser (CIA), which depends on segmentation and weighting of handcrafted features, to a CNN trained by deep...
| Published in: | European Journal of Cancer Vol. 135; pp. 39 - 47 |
|---|---|
| Main Authors: | , , , , , , , , , |
| Format: | research tables/charts Journal Article |
| Published: |
Pergamon Press - An Imprint of Elsevier Science
Aug2020
|
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=144622341&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 144622341 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09598049 1VW jtl: European Journal of Cancer issn: 09598049 maglogo: N pubinfo: dt: Aug2020 vid: 135 pid: 2410 pub: Pergamon Press - An Imprint of Elsevier Science artinfo: ui: 144622341 144622341 144622341 10.1016/j.ejca.2020.04.043 144622341 ppf: 39 ppct: 8 formats: tig: atl: Past and present of computer-assisted dermoscopic diagnosis: performance of a conventional image analyser versus a convolutional neural network in a prospective data set of 1,981 skin lesions. aug: au: Sies, Katharina Winkler, Julia K. Fink, Christine Bardehle, Felicitas Toberer, Ferdinand Buhl, Timo Enk, Alexander Blum, Andreas Rosenberger, Albert Haenssle, Holger A. affil: Department of Dermatology, University of Heidelberg, Heidelberg, Germany sug: subj: Skin Neoplasms Diagnosis Image Interpretation, Computer Assisted Methods Deep Learning Neural Networks (Computer) Predictive Value of Tests Human Descriptive Statistics Prospective Studies Image Interpretation, Computer Assisted Equipment and Supplies Cross Sectional Studies Male Female Middle Age Comparative Studies Confidence Intervals Sensitivity and Specificity ROC Curve Melanoma Algorithms Skin Neoplasms Classification Middle Aged: 45-64 years Male Female ab: Convolutional neural networks (CNNs) have shown a dermatologist-level performance in the classification of skin lesions. We aimed to deliver a head-to-head comparison of a conventional image analyser (CIA), which depends on segmentation and weighting of handcrafted features, to a CNN trained by deep learning. Cross-sectional study using a real-world, prospectively acquired, dermoscopic dataset of 1981 skin lesions to compare the diagnostic performance of a market-approved CNN (Moleanalyzer-Pro™, developed in 2018) to a CIA (Moleanalyzer-3™/Dynamole™; developed in 2004, all FotoFinder Systems Inc, Germany). As a reference standard, we used histopathological diagnoses (n = 785) or, in non-excised benign lesions (n = 1196), expert consensus plus an uneventful follow-up by sequential digital dermoscopy for at least 2 years. A total of 281 malignant lesions and 1700 benign lesions from 435 patients (62.2% male, mean age: 52 years) were prospectively imaged. The CNN showed a sensitivity of 77.6% (95% confidence interval [CI]: [72.4%–82.1%]), specificity of 95.3% (95% CI: [94.2%–96.2%]), and receiver operating characteristic (ROC)-area under the curve (AUC) of 0.945 (95% CI: [0.930–0.961]). In contrast, the CIA achieved a sensitivity of 53.4% (95% CI: [47.5%–59.1%]), specificity of 86.6% (95% CI: [84.9%–88.1%]) and ROC-AUC of 0.738 (95% CI: [0.701–0.774]). The data set included melanomas originally diagnosed by dynamic changes during sequential digital dermoscopy (52 of 201, 20.6%), which reduced the sensitivities of both classifiers. Pairwise comparisons of sensitivities, specificities, and ROC-AUCs indicated a clear outperformance by the CNN (all p < 0.001). The superior diagnostic performance of the CNN argues against a continued application of former CIAs as an aide to physicians' clinical management decisions. • We compared two market-approved computer-algorithms for skin cancer detection. • A conventional image analyser (CIA) was compared with a deep learning convolutional neural network (CNN). • The CNN significantly outperformed the CIA in sensitivity, specificity, and area under the receiver operating characteristic curve. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|