Patient acceptance and trust in automated computer‐assisted diagnosis of melanoma with dermatofluoroscopy

Fink, Christine; Uhlmann, Lorenz; Hofmann, Maja; Forschner, Andrea; Eigentler, Thomas; Garbe, Claus; Enk, Alexander; Haenssle, Holger A.

doi:10.1111/ddg.13562

Cited by 20 publications

(16 citation statements)

References 15 publications

(25 reference statements)

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“…Therefore, adding the AI classifier to the routine diagnostic procedures already in place in the clinic and exploring its effects is an interesting next step. Completely taking the physician out of the loop is questionable as neural networks are known to have robustness issues and while patients may accept the usage of CAD systems, it is tied to the condition that physicians interpret the results of such systems and are not replaced by them [ 22 ]. Furthermore, the small sample size of the participants does not reflect the experience levels encountered in the clinic (eg, no trainee doctors) but was consciously chosen to ensure conscientious participation.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence and Its Effect on Dermatologists’ Accuracy in Dermoscopic Melanoma Image Classification: Web-Based Survey Study

Maron¹,

Utikal²,

Hekler³

et al. 2020

J Med Internet Res

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Background Early detection of melanoma can be lifesaving but this remains a challenge. Recent diagnostic studies have revealed the superiority of artificial intelligence (AI) in classifying dermoscopic images of melanoma and nevi, concluding that these algorithms should assist a dermatologist’s diagnoses. Objective The aim of this study was to investigate whether AI support improves the accuracy and overall diagnostic performance of dermatologists in the dichotomous image–based discrimination between melanoma and nevus. Methods Twelve board-certified dermatologists were presented disjoint sets of 100 unique dermoscopic images of melanomas and nevi (total of 1200 unique images), and they had to classify the images based on personal experience alone (part I) and with the support of a trained convolutional neural network (CNN, part II). Additionally, dermatologists were asked to rate their confidence in their final decision for each image. Results While the mean specificity of the dermatologists based on personal experience alone remained almost unchanged (70.6% vs 72.4%; P=.54) with AI support, the mean sensitivity and mean accuracy increased significantly (59.4% vs 74.6%; P=.003 and 65.0% vs 73.6%; P=.002, respectively) with AI support. Out of the 10% (10/94; 95% CI 8.4%-11.8%) of cases where dermatologists were correct and AI was incorrect, dermatologists on average changed to the incorrect answer for 39% (4/10; 95% CI 23.2%-55.6%) of cases. When dermatologists were incorrect and AI was correct (25/94, 27%; 95% CI 24.0%-30.1%), dermatologists changed their answers to the correct answer for 46% (11/25; 95% CI 33.1%-58.4%) of cases. Additionally, the dermatologists’ average confidence in their decisions increased when the CNN confirmed their decision and decreased when the CNN disagreed, even when the dermatologists were correct. Reported values are based on the mean of all participants. Whenever absolute values are shown, the denominator and numerator are approximations as every dermatologist ended up rating a varying number of images due to a quality control step. Conclusions The findings of our study show that AI support can improve the overall accuracy of the dermatologists in the dichotomous image–based discrimination between melanoma and nevus. This supports the argument for AI-based tools to aid clinicians in skin lesion classification and provides a rationale for studies of such classifiers in real-life settings, wherein clinicians can integrate additional information such as patient age and medical history into their decisions.

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Section: Discussionmentioning

confidence: 99%

Artificial Intelligence and Its Effect on Dermatologists’ Accuracy in Dermoscopic Melanoma Image Classification: Web-Based Survey Study

Maron¹,

Utikal²,

Hekler³

et al. 2020

J Med Internet Res

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“…Similar results were obtained in a large international survey among dermatologists, indicating that AI is well-accepted in the dermatology field: The majority of participants agreed or strongly agreed that AI will improve dermatology and felt that AI should be a part of medical training ( 17 ). A survey on computer-assisted diagnosis of melanomas with dermatofluoroscopy also showed a high acceptance of that technology among patients ( 18 ). A Swedish survey among participants in a breast cancer screening program about AI-based automated mammogram analysis showed a high level of confidence in computer-aided decision making ( 19 ).…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence in Skin Cancer Diagnostics: The Patients' Perspective

et al. 2020

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Background: Artificial intelligence (AI) has shown promise in numerous experimental studies, particularly in skin cancer diagnostics. Translation of these findings into the clinic is the logical next step. This translation can only be successful if patients' concerns and questions are addressed suitably. We therefore conducted a survey to evaluate the patients' view of artificial intelligence in melanoma diagnostics in Germany, with a particular focus on patients with a history of melanoma. Participants and Methods: A web-based questionnaire was designed using LimeSurvey, sent by e-mail to university hospitals and melanoma support groups and advertised on social media. The anonymous questionnaire evaluated patients' expectations and concerns toward artificial intelligence in general as well as their attitudes toward different application scenarios. Descriptive analysis was performed with expression of categorical variables as percentages and 95% confidence intervals. Statistical tests were performed to investigate associations between sociodemographic data and selected items of the questionnaire. Results: 298 individuals (154 with a melanoma diagnosis, 143 without) responded to the questionnaire. About 94% [95% CI = 0.91-0.97] of respondents supported the use of artificial intelligence in medical approaches. 88% [95% CI = 0.85-0.92] would even make their own health data anonymously available for the further development of AI-based applications in medicine. Only 41% [95% CI = 0.35-0.46] of respondents were amenable to the use of artificial intelligence as stand-alone system, 94% [95% CI = 0.92-0.97] to its use as assistance system for physicians. In subgroup analyses, only minor differences were detectable. Respondents with a previous history of melanoma were more amenable to the use of AI applications for early detection even at home. They would prefer an application scenario where physician and AI classify the lesions independently. With respect to AI-based applications in medicine, patients were concerned about insufficient Jutzi et al. Patients' View on AI-Based Diagnosis data protection, impersonality and susceptibility to errors, but expected faster, more precise and unbiased diagnostics, less diagnostic errors and support for physicians. Conclusions: The vast majority of participants exhibited a positive attitude toward the use of artificial intelligence in melanoma diagnostics, especially as an assistance system.

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“…Es konnte geschlussfolgert werden, dass es sich bei der Dermatofluoroskopie um eine sichere Technologie mit guter diagnostischer Leistungsfähigkeit handelt [5]. Zusätzlich wurden Patienten im Rahmen der Zulassungsstudie hinsichtlich ihrer Akzeptanz und ihres Vertrauens in die Dermatofluoroskopie befragt [7]. 74 % der untersuchten Patienten zeigten eine hohe bis sehr hohe Akzeptanz gegenüber der Me-thode.…”

Section: Sicherheit Und Diagnostische Leistungsfähigkeit Des Dermafc ®unclassified

“…Die Mehrheit (78,5 %) der Patienten war der Meinung, dass eine computergestützte Diagnostik grundsätzlich die diagnostische Leistung eines Arztes verbessern kann. Jedoch forderten alle befragten Patienten mit hoher oder sehr hoher Zustimmung zusätzlich zur Dermatofluoroskopie die Meinung des Arztes [7]. Zuletzt wurden im Rahmen einer Post-Market-Clinical-Follow-up-Studie retrospektiv Patientendaten aus der Zulassungsstudie (FLIMMA-Studie) untersucht [8,9].…”

Section: Sicherheit Und Diagnostische Leistungsfähigkeit Des Dermafc ®unclassified

Dermatofluoroskopie – Ist angeregtes Melanin anregend?

Fink

2020

Akt Dermatol

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ZusammenfassungDie Technologie der Dermatofluoroskopie beruht auf einer selektiven Anregung der Melaninfluoreszenz durch stufenweise 2-Photonen-Absorption. Hierbei wird das Melanin in einer Spektralregion angeregt, in der keine andere Hautkomponente absorbiert. Dadurch wird die Melaninfluoreszenz erstmals messbar. Unter Verwendung dieser Technik wird die Läsion Punkt für Punkt abgetastet. Die Fluoreszenzsignale werden dann mithilfe eines auf künstlicher Intelligenz basierten Algorithmus analysiert und das Ergebnis Score-basiert dargestellt. In Studien zeigte die Dermatofluoroskopie eine hohe diagnostische Genauigkeit mit guter Sensitivität und Spezifität. Das Medizinprodukt DermaFC® der Klasse IIa ist seit 2017 CE-zertifiziert und für den Einsatz als Diagnoseunterstützung bei melanozytären melanomverdächtigen Läsionen zugelassen. Nicht geeignet ist die Dermatofluoroskopie bei schwach pigmentierten Läsionen sowie schlecht zugänglichen Lokalisationen.

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Patient acceptance and trust in automated computer‐assisted diagnosis of melanoma with dermatofluoroscopy

Abstract: Patient acceptance and trust in dermatofluoroscopy was high. Patients clearly supported the use of automated, computer-assisted diagnostics as an adjunct to the physicians' examination.

Cited by 20 publications

References 15 publications

Artificial Intelligence and Its Effect on Dermatologists’ Accuracy in Dermoscopic Melanoma Image Classification: Web-Based Survey Study

Artificial Intelligence and Its Effect on Dermatologists’ Accuracy in Dermoscopic Melanoma Image Classification: Web-Based Survey Study

Artificial Intelligence in Skin Cancer Diagnostics: The Patients' Perspective

Dermatofluoroskopie – Ist angeregtes Melanin anregend?

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