Deep learning convolutional neural networks (CNN) may facilitate melanoma detection, but data comparing a CNN’s diagnostic performance to larger groups of dermatologists are lacking.
Google’s ...Inception v4 CNN architecture was trained and validated using dermoscopic images and corresponding diagnoses. In a comparative cross-sectional reader study a 100-image test-set was used (level-I: dermoscopy only; level-II: dermoscopy plus clinical information and images). Main outcome measures were sensitivity, specificity and area under the curve (AUC) of receiver operating characteristics (ROC) for diagnostic classification (dichotomous) of lesions by the CNN versus an international group of 58 dermatologists during level-I or -II of the reader study. Secondary end points included the dermatologists’ diagnostic performance in their management decisions and differences in the diagnostic performance of dermatologists during level-I and -II of the reader study. Additionally, the CNN’s performance was compared with the top-five algorithms of the 2016 International Symposium on Biomedical Imaging (ISBI) challenge.
In level-I dermatologists achieved a mean (±standard deviation) sensitivity and specificity for lesion classification of 86.6% (±9.3%) and 71.3% (±11.2%), respectively. More clinical information (level-II) improved the sensitivity to 88.9% (±9.6%, P=0.19) and specificity to 75.7% (±11.7%, P<0.05). The CNN ROC curve revealed a higher specificity of 82.5% when compared with dermatologists in level-I (71.3%, P<0.01) and level-II (75.7%, P<0.01) at their sensitivities of 86.6% and 88.9%, respectively. The CNN ROC AUC was greater than the mean ROC area of dermatologists (0.86 versus 0.79, P<0.01). The CNN scored results close to the top three algorithms of the ISBI 2016 challenge.
For the first time we compared a CNN’s diagnostic performance with a large international group of 58 dermatologists, including 30 experts. Most dermatologists were outperformed by the CNN. Irrespective of any physicians’ experience, they may benefit from assistance by a CNN’s image classification.
This study was registered at the German Clinical Trial Register (DRKS-Study-ID: DRKS00013570; https://www.drks.de/drks_web/).
Zusammenfassung
Vorgestellt wird eine 30-jährige Patientin, die seit Jahren an initial unspezifischen Symptomen, wie rezidivierenden, nicht allergischen und nicht infektiösen Sinusitiden, „late ...onset“ Asthma bronchiale und ausgeprägter Lymphadenopathie litt. Erst nach Auftreten erster Hauterscheinungen konnte durch die Entnahme einer Probebiopsie die Diagnose einer eosinophilen granulomatösen Polyangiitis (EGPA) gestellt werden. Die EGPA ist eine schwerwiegende Systemerkrankung, die unbehandelt multiple Organschäden verursachen und sogar einen tödlichen Verlauf nehmen kann. Mit der adäquaten Behandlung verläuft die Erkrankung in mehr als 90 % der Fälle milde und kann oft sogar vollständig in Remission gehen. Durch die richtige Diagnosestellung konnte die Patientin adäquat und erfolgreich behandelt und das Risiko für Spätmanifestationen und Folgeschäden mit potenziell tödlichem Verlauf vermindert werden.
The clinical differentiation of face and scalp lesions (FSLs) is challenging even for trained dermatologists. Studies comparing the diagnostic performance of a convolutional neural network (CNN) with ...dermatologists in FSL are lacking.
A market-approved CNN (Moleanalyzer-Pro, FotoFinder Systems) was used for binary classifications of 100 dermoscopic images of FSL. The same lesions were used in a two-level reader study including 64 dermatologists (level I: dermoscopy only; level II: dermoscopy, clinical close-up images, textual information). Primary endpoints were the CNN's sensitivity and specificity in comparison with the dermatologists' management decisions in level II. Generalizability of the CNN results was tested by using four additional external data sets.
The CNN's sensitivity, specificity and ROC AUC were 96.2% 87.0%–98.9%, 68.8% 54.7%–80.1% and 0.929 0.880–0.978, respectively. In level II, the dermatologists' management decisions showed a mean sensitivity of 84.2% 82.2%–86.2% and specificity of 69.4% 66.0%–72.8%. When fixing the CNN's specificity at the dermatologists' mean specificity (69.4%), the CNN's sensitivity (96.2% 87.0%–98.9%) was significantly higher than that of dermatologists (84.2% 82.2%–86.2%; p < 0.001). Dermatologists of all training levels were outperformed by the CNN (all p < 0.001). In confirmation, the CNN's accuracy (83.0%) was significantly higher than dermatologists' accuracies in level II management decisions (all p < 0.001). The CNN's performance was largely confirmed in three additional external data sets but particularly showed a reduced specificity in one Australian data set including FSL on severely sun-damaged skin.
When applied as an assistant system, the CNN's higher sensitivity at an equivalent specificity may result in an improved early detection of face and scalp skin cancers.
•Face and scalp lesions (FSLs) are difficult to diagnose.•Physicians may benefit from assistance by convolutional neural networks (CNNs).•In a data set of 100 FSL cases a CNN was compared with 64 dermatologists.•The CNN outperformed dermatologists by a higher sensitivity at equivalent specificity.•The CNN may help to improve skin cancer detection in clinical routine.
Highlights • apremilast reduces TNF-α and IL-12 but enhances IL-23 and IL-1ß production by slanDCs. • apremilast-treated slanDCs induced enhanced Th17 T cell responses.
PDF
