101
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Kutzner H, Jutzi TB, Krahl D, Krieghoff‐Henning EI, Heppt MV, Hekler A, Schmitt M, Maron RCR, Fröhling S, Kalle C, Brinker TJ. Überdiagnose von Melanomen – Ursachen, Konsequenzen und Lösungsansätze. J Dtsch Dermatol Ges 2020; 18:1236-1244. [DOI: 10.1111/ddg.14233_g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2019] [Accepted: 04/13/2020] [Indexed: 11/28/2022]
Affiliation(s)
| | - Tanja B. Jutzi
- Nachwuchsgruppe Digitale Biomarker für die Onkologie (DBO), Nationales Centrum für Tumorerkrankungen (NCT) Deutsches Krebsforschungszentrum (DKFZ) Heidelberg
| | - Dieter Krahl
- Privates Labor für Dermatohistopathologie Mönchhofstraße 52 Heidelberg
| | - Eva I. Krieghoff‐Henning
- Nachwuchsgruppe Digitale Biomarker für die Onkologie (DBO), Nationales Centrum für Tumorerkrankungen (NCT) Deutsches Krebsforschungszentrum (DKFZ) Heidelberg
| | | | - Achim Hekler
- Nachwuchsgruppe Digitale Biomarker für die Onkologie (DBO), Nationales Centrum für Tumorerkrankungen (NCT) Deutsches Krebsforschungszentrum (DKFZ) Heidelberg
| | - Max Schmitt
- Nachwuchsgruppe Digitale Biomarker für die Onkologie (DBO), Nationales Centrum für Tumorerkrankungen (NCT) Deutsches Krebsforschungszentrum (DKFZ) Heidelberg
| | - Roman C. R. Maron
- Nachwuchsgruppe Digitale Biomarker für die Onkologie (DBO), Nationales Centrum für Tumorerkrankungen (NCT) Deutsches Krebsforschungszentrum (DKFZ) Heidelberg
| | - Stefan Fröhling
- Nachwuchsgruppe Digitale Biomarker für die Onkologie (DBO), Nationales Centrum für Tumorerkrankungen (NCT) Deutsches Krebsforschungszentrum (DKFZ) Heidelberg
| | - Christof Kalle
- Berlin Institute of Health (BIH) und Charité – Universitätsmedizin Berlin
| | - Titus J. Brinker
- Nachwuchsgruppe Digitale Biomarker für die Onkologie (DBO), Nationales Centrum für Tumorerkrankungen (NCT) Deutsches Krebsforschungszentrum (DKFZ) Heidelberg
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102
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Blum A, Bosch S, Haenssle HA, Fink C, Hofmann-Wellenhof R, Zalaudek I, Kittler H, Tschandl P. [Artificial intelligence and smartphone program applications (Apps) : Relevance for dermatological practice]. Hautarzt 2020; 71:691-698. [PMID: 32720165 DOI: 10.1007/s00105-020-04658-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
ADVANTAGES OF ARTIFICIAL INTELLIGENCE (AI) With responsible, safe and successful use of artificial intelligence (AI), possible advantages in the field of dermato-oncology include the following: (1) medical work can focus on skin cancer patients, (2) patients can be more quickly and effectively treated despite the increasing incidence of skin cancer and the decreasing number of actively working dermatologists and (3) users can learn from the AI results. POTENTIAL DISADVANTAGES AND RISKS OF AI USE: (1) Lack of mutual trust can develop due to the decreased patient-physician contact, (2) additional time effort will be necessary to promptly evaluate the AI-classified benign lesions, (3) lack of adequate medical experience to recognize misclassified AI decisions and (4) recontacting a patient in due time in the case of incorrect AI classifications. Still problematic in the use of AI are the medicolegal situation and remuneration. Apps using AI currently cannot provide sufficient assistance based on clinical images of skin cancer. REQUIREMENTS AND POSSIBLE USE OF SMARTPHONE PROGRAM APPLICATIONS Smartphone program applications (apps) can be implemented responsibly when the image quality is good, the patient's history can be entered easily, transmission of the image and results are assured and medicolegal aspects as well as remuneration are clarified. Apps can be used for disease-specific information material and can optimize patient care by using teledermatology.
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Affiliation(s)
- A Blum
- Hautarzt- und Lehrpraxis, Augustinerplatz 7, 78462, Konstanz, Deutschland.
| | - S Bosch
- Hautarztpraxis, Ludwigsburg, Deutschland
| | - H A Haenssle
- Universitäts-Hautklinik Heidelberg, Heidelberg, Deutschland
| | - C Fink
- Universitäts-Hautklinik Heidelberg, Heidelberg, Deutschland
| | - R Hofmann-Wellenhof
- Universitätsklinik für Dermatologie, Medizinische Universität Graz, Graz, Österreich
| | - I Zalaudek
- Dermatology Clinic, University Hospital of Trieste, Hospital Maggiore, Trieste, Italien
| | - H Kittler
- Universitätsklinik für Dermatologie, Medizinische Universität Wien, Wien, Österreich
| | - P Tschandl
- Universitätsklinik für Dermatologie, Medizinische Universität Wien, Wien, Österreich
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103
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Kong FW, Horsham C, Ngoo A, Soyer HP, Janda M. Review of smartphone mobile applications for skin cancer detection: what are the changes in availability, functionality, and costs to users over time? Int J Dermatol 2020; 60:289-308. [PMID: 32880938 DOI: 10.1111/ijd.15132] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 06/08/2020] [Accepted: 07/20/2020] [Indexed: 12/15/2022]
Abstract
Smartphone applications (apps) are available to consumers for skin cancer prevention and early detection. This study aims to review changes over time in the skin cancer apps available to consumers as well as their functionality and costs. Apps for the prevention of skin cancer were searched on two major smartphone app stores (Android and iOS) in June 2019. The number, functionality, ratings, and price of the apps were described and compared to similar reviews of the skin cancer app market from 2014 to 2017. Overall, the June 2019 search identified 66 apps. Of 39 apps found in 2014, 30 were no longer available in 2019 representing an attrition rate of 77%; of 43 apps available in 2017, attrition was 46.5%. In 2019, 63.6% (n = 42/66) of apps were free to download compared to 53.5% (n = 23/43) in 2017. Input from clinician/professional bodies was evident for 47.0% (n = 31/66) of the apps in 2019 compared to 34.9% (15/43) in 2017. The most common app functionality offered in 2019 was monitoring/tracking of lesions at 48.5% (n = 32/66). Since 2014, there has been a steady increase in the number of apps available for the general public to support the prevention or early detection of skin cancers. There continues to be a high turnover of apps, and many apps still appear to lack clinician input and/or evidence for their safety and value.
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Affiliation(s)
- Fleur W Kong
- Dermatology Research Centre, The University of Queensland Diamantina Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Caitlin Horsham
- Centre of Health Services Research, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Alexander Ngoo
- Dermatology Research Centre, The University of Queensland Diamantina Institute, The University of Queensland, Brisbane, QLD, Australia
| | - H Peter Soyer
- Dermatology Research Centre, The University of Queensland Diamantina Institute, The University of Queensland, Brisbane, QLD, Australia.,Department of Dermatology, Princess Alexandra Hospital, Brisbane, QLD, Australia
| | - Monika Janda
- Centre of Health Services Research, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
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104
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Kutzner H, Jutzi TB, Krahl D, Krieghoff-Henning EI, Heppt MV, Hekler A, Schmitt M, Maron RCR, Fröhling S, von Kalle C, Brinker TJ. Overdiagnosis of melanoma - causes, consequences and solutions. J Dtsch Dermatol Ges 2020; 18:1236-1243. [PMID: 32841508 DOI: 10.1111/ddg.14233] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2019] [Accepted: 04/13/2020] [Indexed: 12/14/2022]
Abstract
Malignant melanoma is the skin tumor that causes most deaths in Germany. At an early stage, melanoma is well treatable, so early detection is essential. However, the skin cancer screening program in Germany has been criticized because although melanomas have been diagnosed more frequently since introduction of the program, the mortality from malignant melanoma has not decreased. This indicates that the observed increase in melanoma diagnoses be due to overdiagnosis, i.e. to the detection of lesions that would never have created serious health problems for the patients. One of the reasons is the challenging distinction between some benign and malignant lesions. In addition, there may be lesions that are biologically equivocal, and other lesions that are classified as malignant according to current criteria, but that grow so slowly that they would never have posed a threat to patient's life. So far, these "indolent" melanomas cannot be identified reliably due to a lack of biomarkers. Moreover, the likelihood that an in-situ melanoma will progress to an invasive tumor still cannot be determined with any certainty. When benign lesions are diagnosed as melanoma, the consequences are unnecessary psychological and physical stress for the affected patients and incurred therapy costs. Vice versa, underdiagnoses in the sense of overlooked melanomas can adversely affect patients' prognoses and may necessitate more intense therapies. Novel diagnostic options could reduce the number of over- and underdiagnoses and contribute to more objective diagnoses in borderline cases. One strategy that has yielded promising results in pilot studies is the use of artificial intelligence-based diagnostic tools. However, these applications still await translation into clinical and pathological routine.
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Affiliation(s)
| | - Tanja B Jutzi
- Digital Biomarkers for Oncology group (DBO), National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Dieter Krahl
- Private Laboratory for Dermatohistopathology, Mönchhofstraße 52, Heidelberg, Germany
| | - Eva I Krieghoff-Henning
- Digital Biomarkers for Oncology group (DBO), National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Markus V Heppt
- Department of Dermatology, University Hospital Erlangen, Erlangen, Germany
| | - Achim Hekler
- Digital Biomarkers for Oncology group (DBO), National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Max Schmitt
- Digital Biomarkers for Oncology group (DBO), National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Roman C R Maron
- Digital Biomarkers for Oncology group (DBO), National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Stefan Fröhling
- Digital Biomarkers for Oncology group (DBO), National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Christof von Kalle
- Berlin Institute of Health (BIH) and Charité-University Medical Center Berlin, Berlin, Germany
| | - Titus J Brinker
- Digital Biomarkers for Oncology group (DBO), National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany
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105
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Greis C, Maul LV, Hsu C, Djamei V, Schmid-Grendelmeier P, Navarini AA. [Artificial intelligence to support telemedicine in Africa]. Hautarzt 2020; 71:686-690. [PMID: 32761386 PMCID: PMC7407433 DOI: 10.1007/s00105-020-04664-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Telemedizin findet seit Jahrzehnten Anwendung im Alltag von Dermatologen. Insbesondere in afrikanischen Ländern mit begrenzter medizinischer Versorgung, zu überbrückenden geografischen Distanzen und einem zwischenzeitlich relativ gut ausgebauten Telekommunikationssektor liegen die Vorteile auf der Hand. Nationale und internationale Arbeitsgruppen unterstützen den Aufbau von teledermatologischen Projekten und bedienen sich in den letzten Jahren zunehmend KI(künstliche Intelligenz)-gestützter Technologien, um Ärzte vor Ort zu unterstützen. Vor diesem Hintergrund stellen ethnische Variationen eine besondere Herausforderung in der Entwicklung automatisierter Algorithmen dar. Um die Genauigkeit der Systeme weiter zu verbessern und globalisieren zu können, ist es wichtig, die Zahl der verfügbaren klinischen Daten zu erhöhen. Dies kann nur mit der aktiven Beteiligung der lokalen Gesundheitsversorger sowie der dermatologischen Gemeinschaft gelingen und muss stets im Interesse des einzelnen Patienten erfolgen.
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Affiliation(s)
- C Greis
- Klinik für Dermatologie, Universitätsspital Zürich, Gloriastr. 31, 8091, Zürich, Schweiz.
| | - L V Maul
- Klinik für Dermatologie, Universitätsspital Basel, Basel, Schweiz
| | - C Hsu
- Klinik für Dermatologie, Universitätsspital Basel, Basel, Schweiz
| | - V Djamei
- Klinik für Dermatologie, Universitätsspital Zürich, Gloriastr. 31, 8091, Zürich, Schweiz
| | - P Schmid-Grendelmeier
- Klinik für Dermatologie, Universitätsspital Zürich, Gloriastr. 31, 8091, Zürich, Schweiz
| | - A A Navarini
- Klinik für Dermatologie, Universitätsspital Basel, Basel, Schweiz
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106
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[Computer-assisted skin cancer diagnosis : Is it time for artificial intelligence in clinical practice?]. Hautarzt 2020; 71:669-676. [PMID: 32747996 DOI: 10.1007/s00105-020-04662-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
BACKGROUND Artificial intelligence (AI) is increasingly being used in medical practice. Especially in the image-based diagnosis of skin cancer, AI shows great potential. However, there is a significant discrepancy between expectations and true relevance of AI in current dermatological practice. OBJECTIVES This article summarizes promising study results of skin cancer diagnosis by computer-based diagnostic systems and discusses their significance for daily practice. We hereby focus on the analysis of dermoscopic images of pigmented and unpigmented skin lesions. MATERIALS AND METHODS A selective literature search for recent relevant trials was conducted. The included studies used machine learning, and in particular "convolutional neural networks", which have been shown to be particularly effective for the classification of image data. RESULTS AND CONCLUSIONS In numerous studies, computer algorithms were able to detect pigmented and nonpigmented neoplasms of the skin with high precision, comparable to that of dermatologists. The combination of the physician's assessment and AI showed the best results. Computer-based diagnostic systems are widely accepted among patients and physicians. However, they are still not applicable in daily practice, since computer-based diagnostic systems have only been tested in an experimental environment. In addition, many digital diagnostic criteria that help AI to classify skin lesions remain unclear. This lack of transparency still needs to be addressed. Moreover, clinical studies on the use of AI-based assistance systems are needed in order to prove its applicability in daily dermatologic practice.
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107
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Young AT, Vora NB, Cortez J, Tam A, Yeniay Y, Afifi L, Yan D, Nosrati A, Wong A, Johal A, Wei ML. The role of technology in melanoma screening and diagnosis. Pigment Cell Melanoma Res 2020; 34:288-300. [PMID: 32558281 DOI: 10.1111/pcmr.12907] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2019] [Revised: 03/31/2020] [Accepted: 06/12/2020] [Indexed: 12/28/2022]
Abstract
Melanoma presents challenges for timely and accurate diagnosis. Expert panels have issued risk-based screening guidelines, with recommended screening by visual inspection. To assess how recent technology can impact the risk/benefit considerations for melanoma screening, we comprehensively reviewed non-invasive visual-based technologies. Dermoscopy increases lesional diagnostic accuracy for both dermatologists and primary care providers; total body photography and sequential digital dermoscopic imaging also increase diagnostic accuracy, are supported by automated lesion detection and tracking, and may be best suited to use by dermatologists for longitudinal follow-up. Specialized imaging modalities using non-visible light technology have unproven benefit over dermoscopy and can be limited by cost, access, and training requirements. Mobile apps facilitate image capture and lesion tracking. Teledermatology has good concordance with face-to-face consultation and increases access, with increased accuracy using dermoscopy. Deep learning models can surpass dermatologist accuracy, but their clinical utility has yet to be demonstrated. Technology-aided diagnosis may change the calculus of screening; however, well-designed prospective trials are needed to assess the efficacy of these different technologies, alone and in combination to support refinement of guidelines for melanoma screening.
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Affiliation(s)
- Albert T Young
- Department of Dermatology, University of California, San Francisco, CA, USA.,Dermatology Service, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA
| | - Niki B Vora
- Dermatology Service, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA
| | - Jose Cortez
- Dermatology Service, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA
| | - Andrew Tam
- Dermatology Service, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA
| | - Yildiray Yeniay
- Department of Dermatology, University of California, San Francisco, CA, USA
| | - Ladi Afifi
- Department of Dermatology, University of California, San Francisco, CA, USA
| | - Di Yan
- Department of Dermatology, University of California, San Francisco, CA, USA
| | - Adi Nosrati
- Department of Dermatology, University of California, San Francisco, CA, USA.,Dermatology Service, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA
| | - Andrew Wong
- Dermatology Service, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA
| | - Arjun Johal
- Department of Dermatology, University of California, San Francisco, CA, USA.,Dermatology Service, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA
| | - Maria L Wei
- Department of Dermatology, University of California, San Francisco, CA, USA.,Dermatology Service, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA
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108
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Lallas A, Lallas K, Tschandl P, Kittler H, Apalla Z, Longo C, Argenziano G. The dermoscopic inverse approach significantly improves the accuracy of human readers for lentigo maligna diagnosis. J Am Acad Dermatol 2020; 84:381-389. [PMID: 32592885 DOI: 10.1016/j.jaad.2020.06.085] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Revised: 06/16/2020] [Accepted: 06/19/2020] [Indexed: 12/24/2022]
Abstract
BACKGROUND A recently introduced dermoscopic method for the diagnosis of early lentigo maligna (LM) is based on the absence of prevalent patterns of pigmented actinic keratosis and solar lentigo/flat seborrheic keratosis. We term this the inverse approach. OBJECTIVE To determine whether training on the inverse approach increases the diagnostic accuracy of readers compared to classic pattern analysis. METHODS We used clinical and dermoscopic images of histopathologically diagnosed LMs, pigmented actinic keratoses, and solar lentigo/flat seborrheic keratoses. Participants in a dermoscopy masterclass classified the lesions at baseline and after training on pattern analysis and the inverse approach. We compared their diagnostic performance among the 3 timepoints and to that of a trained convolutional neural network. RESULTS The mean sensitivity for LM without training was 51.5%; after training on pattern analysis, it increased to 56.7%; and after learning the inverse approach, it increased to 83.6%. The mean proportions of correct answers at the 3 timepoints were 62.1%, 65.5, and 78.5%. The percentages of readers outperforming the convolutional neural network were 6.4%, 15.4%, and 53.9%, respectively. LIMITATIONS The experimental setting and the inclusion of histopathologically diagnosed lesions only. CONCLUSIONS The inverse approach, added to the classic pattern analysis, significantly improves the sensitivity of human readers for early LM diagnosis.
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Affiliation(s)
- Aimilios Lallas
- First Department of Dermatology, Aristotle University, Thessaloniki, Greece.
| | | | - Philipp Tschandl
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Harald Kittler
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Zoe Apalla
- Second Department of Dermatology, Aristotle University, Thessaloniki, Greece
| | - Caterina Longo
- Centro Oncologico ad Alta Tecnologia Diagnostica, Azienda Unità Sanitaria Locale-Istituto di Ricovero e Cura a Carattere Scientifico di Reggio Emilia, Reggio Emilia, Italy; Department of Dermatology, University of Modena and Reggio Emilia, Modena, Italy
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109
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Kim YJ, Han SS, Yang HJ, Chang SE. Prospective, comparative evaluation of a deep neural network and dermoscopy in the diagnosis of onychomycosis. PLoS One 2020; 15:e0234334. [PMID: 32525908 PMCID: PMC7289382 DOI: 10.1371/journal.pone.0234334] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Accepted: 05/22/2020] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Onychomycosis is the most common nail disorder and is associated with diagnostic challenges. Emerging non-invasive, real-time techniques such as dermoscopy and deep convolutional neural networks have been proposed for the diagnosis of this condition. However, comparative studies of the two tools in the diagnosis of onychomycosis have not previously been conducted. OBJECTIVES This study evaluated the diagnostic abilities of a deep neural network (http://nail.modelderm.com) and dermoscopic examination in patients with onychomycosis. METHODS A prospective observational study was performed in patients presenting with dystrophic features in the toenails. Clinical photographs were taken by research assistants, and the ground truth was determined either by direct microscopy using the potassium hydroxide test or by fungal culture. Five board-certified dermatologists determined a diagnosis of onychomycosis using the clinical photographs. The diagnosis was also made using the algorithm and dermoscopic examination. RESULTS A total of 90 patients (mean age, 55.3; male, 43.3%) assessed between September 2018 and July 2019 were included in the analysis. The detection of onychomycosis using the algorithm (AUC, 0.751; 95% CI, 0.646-0.856) and that by dermoscopy (AUC, 0.755; 95% CI, 0.654-0.855) were seen to be comparable (Delong's test; P = 0.952). The sensitivity and specificity of the algorithm at the operating point were 70.2% and 72.7%, respectively. The sensitivity and specificity of diagnosis by the five dermatologists were 73.0% and 49.7%, respectively. The Youden index of the algorithm (0.429) was also comparable to that of the dermatologists' diagnosis (0.230±0.176; Wilcoxon rank-sum test; P = 0.667). CONCLUSIONS As a standalone method, the algorithm analyzed photographs taken by non-physician and showed comparable accuracy for the diagnosis of onychomycosis to that made by experienced dermatologists and by dermoscopic examination. Large sample size and world-wide, multicentered studies should be investigated to prove the performance of the algorithm.
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Affiliation(s)
- Young Jae Kim
- Department of Dermatology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Seung Seog Han
- Department of Dermatology, I Dermatology Clinic, Seoul, Korea
- * E-mail: (SEC); (SSH)
| | - Hee Joo Yang
- Department of Dermatology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Sung Eun Chang
- Department of Dermatology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
- * E-mail: (SEC); (SSH)
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110
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Heppt M, Berking C. The value of convolutional neural networks in the diagnosis of melanoma simulators. J Eur Acad Dermatol Venereol 2020; 34:1134-1135. [DOI: 10.1111/jdv.16577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Accepted: 04/29/2020] [Indexed: 11/29/2022]
Affiliation(s)
- M.V. Heppt
- Department of Dermatology Universitätsklinikum Erlangen Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU) Erlangen Germany
| | - C. Berking
- Department of Dermatology Universitätsklinikum Erlangen Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU) Erlangen Germany
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111
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Hekler A, Kather JN, Krieghoff-Henning E, Utikal JS, Meier F, Gellrich FF, Upmeier Zu Belzen J, French L, Schlager JG, Ghoreschi K, Wilhelm T, Kutzner H, Berking C, Heppt MV, Haferkamp S, Sondermann W, Schadendorf D, Schilling B, Izar B, Maron R, Schmitt M, Fröhling S, Lipka DB, Brinker TJ. Effects of Label Noise on Deep Learning-Based Skin Cancer Classification. Front Med (Lausanne) 2020; 7:177. [PMID: 32435646 PMCID: PMC7218064 DOI: 10.3389/fmed.2020.00177] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Accepted: 04/16/2020] [Indexed: 11/19/2022] Open
Abstract
Recent studies have shown that deep learning is capable of classifying dermatoscopic images at least as well as dermatologists. However, many studies in skin cancer classification utilize non-biopsy-verified training images. This imperfect ground truth introduces a systematic error, but the effects on classifier performance are currently unknown. Here, we systematically examine the effects of label noise by training and evaluating convolutional neural networks (CNN) with 804 images of melanoma and nevi labeled either by dermatologists or by biopsy. The CNNs are evaluated on a test set of 384 images by means of 4-fold cross validation comparing the outputs with either the corresponding dermatological or the biopsy-verified diagnosis. With identical ground truths of training and test labels, high accuracies with 75.03% (95% CI: 74.39–75.66%) for dermatological and 73.80% (95% CI: 73.10–74.51%) for biopsy-verified labels can be achieved. However, if the CNN is trained and tested with different ground truths, accuracy drops significantly to 64.53% (95% CI: 63.12–65.94%, p < 0.01) on a non-biopsy-verified and to 64.24% (95% CI: 62.66–65.83%, p < 0.01) on a biopsy-verified test set. In conclusion, deep learning methods for skin cancer classification are highly sensitive to label noise and future work should use biopsy-verified training images to mitigate this problem.
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Affiliation(s)
- Achim Hekler
- National Center for Tumor Diseases, German Cancer Research Center, Heidelberg, Germany
| | - Jakob N Kather
- National Center for Tumor Diseases, German Cancer Research Center, Heidelberg, Germany.,Department of Medicine III, RWTH University Hospital Aachen, Aachen, Germany
| | - Eva Krieghoff-Henning
- National Center for Tumor Diseases, German Cancer Research Center, Heidelberg, Germany
| | - Jochen S Utikal
- Department of Dermatology, Heidelberg University, Mannheim, Germany.,Skin Cancer Unit, German Cancer Research Center, Heidelberg, Germany
| | - Friedegund Meier
- Skin Cancer Center at the University Cancer Centre and National Center for Tumor Diseases Dresden, Dresden, Germany.,Department of Dermatology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Frank F Gellrich
- Skin Cancer Center at the University Cancer Centre and National Center for Tumor Diseases Dresden, Dresden, Germany.,Department of Dermatology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | | | - Lars French
- Department of Dermatology and Allergology, Ludwig Maximilian University of Munich, Munich, Germany
| | - Justin G Schlager
- Department of Dermatology and Allergology, Ludwig Maximilian University of Munich, Munich, Germany
| | - Kamran Ghoreschi
- Department of Dermatology, Venereology and Allergology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Tabea Wilhelm
- Department of Dermatology, Venereology and Allergology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Heinz Kutzner
- Dermatopathology Laboratory, Friedrichshafen, Germany
| | - Carola Berking
- Department of Dermatology, University Hospital Erlangen, Erlangen, Germany
| | - Markus V Heppt
- Department of Dermatology, University Hospital Erlangen, Erlangen, Germany
| | - Sebastian Haferkamp
- Department of Dermatology, University Hospital Regensburg, Regensburg, Germany
| | - Wiebke Sondermann
- Department of Dermatology, University Hospital Essen, Essen, Germany
| | - Dirk Schadendorf
- Department of Dermatology, University Hospital Essen, Essen, Germany
| | - Bastian Schilling
- Department of Dermatology, University Hospital Würzburg, Würzburg, Germany
| | - Benjamin Izar
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, United States
| | - Roman Maron
- National Center for Tumor Diseases, German Cancer Research Center, Heidelberg, Germany
| | - Max Schmitt
- National Center for Tumor Diseases, German Cancer Research Center, Heidelberg, Germany
| | - Stefan Fröhling
- National Center for Tumor Diseases, German Cancer Research Center, Heidelberg, Germany.,Translational Cancer Epigenomics, Division of Translational Medical Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Daniel B Lipka
- National Center for Tumor Diseases, German Cancer Research Center, Heidelberg, Germany.,Translational Cancer Epigenomics, Division of Translational Medical Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany.,Faculty of Medicine, Medical Center, Otto-von-Guericke-University, Magdeburg, Germany
| | - Titus J Brinker
- National Center for Tumor Diseases, German Cancer Research Center, Heidelberg, Germany
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Artificial Intelligence in Dermatology: A Primer. J Invest Dermatol 2020; 140:1504-1512. [PMID: 32229141 DOI: 10.1016/j.jid.2020.02.026] [Citation(s) in RCA: 78] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Revised: 02/22/2020] [Accepted: 02/25/2020] [Indexed: 01/17/2023]
Abstract
Artificial intelligence is becoming increasingly important in dermatology, with studies reporting accuracy matching or exceeding dermatologists for the diagnosis of skin lesions from clinical and dermoscopic images. However, real-world clinical validation is currently lacking. We review dermatological applications of deep learning, the leading artificial intelligence technology for image analysis, and discuss its current capabilities, potential failure modes, and challenges surrounding performance assessment and interpretability. We address the following three primary applications: (i) teledermatology, including triage for referral to dermatologists; (ii) augmenting clinical assessment during face-to-face visits; and (iii) dermatopathology. We discuss equity and ethical issues related to future clinical adoption and recommend specific standardization of metrics for reporting model performance.
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