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de Luzuriaga AR. Improving data participation for the development of artificial intelligence in dermatology. Clin Dermatol 2024; 42:447-450. [PMID: 38909859 DOI: 10.1016/j.clindermatol.2024.06.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/25/2024]
Abstract
Artificial intelligence (AI) has the potential to significantly impact many aspects of dermatology. The visual nature of dermatology lends itself to innovations in this space. The robustness of AI algorithms depends on the quality, quantity, and variety of data on which it is trained and tested. Image collections can suffer from inconsistencies in image quality, underrepresentation of various anatomic sites and skin tones, and lack of benign counterparts leading to underperformance of algorithms in contexts other than one in which it is developed. Access to care, trust, rights, control, and transparency all play roles in the willingness of patients and health care providers and systems to collect, provide, and share data. Opportunities to improve data participation for the development of AI include the establishment of data hubs and public algorithms, federated learning strategies, development of renumeration ecosystems for patients and systems, and development of criteria and mechanisms for transparency.
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Affiliation(s)
- Arlene Ruiz de Luzuriaga
- Section of Dermatology, Department of Medicine, University of Chicago, Pritzker School of Medicine, Chicago, Illinois, USA.
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Miller I, Rosic N, Stapelberg M, Hudson J, Coxon P, Furness J, Walsh J, Climstein M. Performance of Commercial Dermatoscopic Systems That Incorporate Artificial Intelligence for the Identification of Melanoma in General Practice: A Systematic Review. Cancers (Basel) 2024; 16:1443. [PMID: 38611119 PMCID: PMC11011068 DOI: 10.3390/cancers16071443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 04/03/2024] [Accepted: 04/04/2024] [Indexed: 04/14/2024] Open
Abstract
BACKGROUND Cutaneous melanoma remains an increasing global public health burden, particularly in fair-skinned populations. Advancing technologies, particularly artificial intelligence (AI), may provide an additional tool for clinicians to help detect malignancies with a more accurate success rate. This systematic review aimed to report the performance metrics of commercially available convolutional neural networks (CNNs) tasked with detecting MM. METHODS A systematic literature search was performed using CINAHL, Medline, Scopus, ScienceDirect and Web of Science databases. RESULTS A total of 16 articles reporting MM were included in this review. The combined number of melanomas detected was 1160, and non-melanoma lesions were 33,010. The performance of market-approved technology and clinician performance for classifying melanoma was highly heterogeneous, with sensitivity ranging from 16.4 to 100.0%, specificity between 40.0 and 98.3% and accuracy between 44.0 and 92.0%. Less heterogeneity was observed when clinicians worked in unison with AI, with sensitivity ranging between 83.3 and 100.0%, specificity between 83.7 and 87.3%, and accuracy between 86.4 and 86.9%. CONCLUSION Instead of focusing on the performance of AI versus clinicians for classifying melanoma, more consistent performance has been obtained when clinicians' work is supported by AI, facilitating management decisions and improving health outcomes.
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Affiliation(s)
- Ian Miller
- Aquatic Based Research, Southern Cross University, Bilinga, QLD 4225, Australia; (I.M.); (N.R.)
- Faculty of Health, Southern Cross University, Bilinga, QLD 4225, Australia (P.C.)
- Specialist Suite, John Flynn Hospital, Tugun, QLD 4224, Australia
| | - Nedeljka Rosic
- Aquatic Based Research, Southern Cross University, Bilinga, QLD 4225, Australia; (I.M.); (N.R.)
- Faculty of Health, Southern Cross University, Bilinga, QLD 4225, Australia (P.C.)
| | - Michael Stapelberg
- Aquatic Based Research, Southern Cross University, Bilinga, QLD 4225, Australia; (I.M.); (N.R.)
- Faculty of Health, Southern Cross University, Bilinga, QLD 4225, Australia (P.C.)
- Specialist Suite, John Flynn Hospital, Tugun, QLD 4224, Australia
| | - Jeremy Hudson
- Faculty of Health, Southern Cross University, Bilinga, QLD 4225, Australia (P.C.)
- North Queensland Skin Centre, Townsville, QLD 4810, Australia
| | - Paul Coxon
- Faculty of Health, Southern Cross University, Bilinga, QLD 4225, Australia (P.C.)
- North Queensland Skin Centre, Townsville, QLD 4810, Australia
| | - James Furness
- Water Based Research Unit, Bond University, Robina, QLD 4226, Australia;
| | - Joe Walsh
- Sport Science Institute, Sydney, NSW 2000, Australia;
- AI Consulting Group, Sydney, NSW 2000, Australia
| | - Mike Climstein
- Aquatic Based Research, Southern Cross University, Bilinga, QLD 4225, Australia; (I.M.); (N.R.)
- Faculty of Health, Southern Cross University, Bilinga, QLD 4225, Australia (P.C.)
- Physical Activity, Lifestyle, Ageing and Wellbeing Faculty Research Group, University of Sydney, Sydney, NSW 2050, Australia
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Duschner N, Baguer DO, Schmidt M, Griewank KG, Hadaschik E, Hetzer S, Wiepjes B, Le'Clerc Arrastia J, Jansen P, Maass P, Schaller J. Applying an artificial intelligence deep learning approach to routine dermatopathological diagnosis of basal cell carcinoma. J Dtsch Dermatol Ges 2023; 21:1329-1337. [PMID: 37814387 DOI: 10.1111/ddg.15180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 06/15/2023] [Indexed: 10/11/2023]
Abstract
BACKGROUND Institutes of dermatopathology are faced with considerable challenges including a continuously rising numbers of submitted specimens and a shortage of specialized health care practitioners. Basal cell carcinoma (BCC) is one of the most common tumors in the fair-skinned western population and represents a major part of samples submitted for histological evaluation. Digitalizing glass slides has enabled the application of artificial intelligence (AI)-based procedures. To date, these methods have found only limited application in routine diagnostics. The aim of this study was to establish an AI-based model for automated BCC detection. PATIENTS AND METHODS In three dermatopathological centers, daily routine practice BCC cases were digitalized. The diagnosis was made both conventionally by analog microscope and digitally through an AI-supported algorithm based on a U-Net architecture neural network. RESULTS In routine practice, the model achieved a sensitivity of 98.23% (center 1) and a specificity of 98.51%. The model generalized successfully without additional training to samples from the other centers, achieving similarly high accuracies in BCC detection (sensitivities of 97.67% and 98.57% and specificities of 96.77% and 98.73% in centers 2 and 3, respectively). In addition, automated AI-based basal cell carcinoma subtyping and tumor thickness measurement were established. CONCLUSIONS AI-based methods can detect BCC with high accuracy in a routine clinical setting and significantly support dermatopathological work.
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Affiliation(s)
| | - Daniel Otero Baguer
- Center for Technical Mathematics (ZeTeM), University of Bremen, Bremen, Germany
| | - Maximilian Schmidt
- Center for Technical Mathematics (ZeTeM), University of Bremen, Bremen, Germany
| | - Klaus Georg Griewank
- Dermatopathologie bei Mainz, Nieder-Olm, Germany
- Department of Dermatology, University Hospital Essen, Essen, Germany
| | - Eva Hadaschik
- MVZ Dermatopathology Duisburg Essen, Essen, Germany
- Department of Dermatology, University Hospital Essen, Essen, Germany
| | - Sonja Hetzer
- MVZ Dermatopathology Duisburg Essen, Essen, Germany
| | | | | | - Philipp Jansen
- Department of Dermatology and Allergology, University Hospital Bonn, Bonn, Germany
| | - Peter Maass
- Center for Technical Mathematics (ZeTeM), University of Bremen, Bremen, Germany
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Duschner N, Baguer DO, Schmidt M, Griewank KG, Hadaschik E, Hetzer S, Wiepjes B, Le'Clerc Arrastia J, Jansen P, Maass P, Schaller J. Einsatz künstlicher Intelligenz mittels Deep Learning in der dermatopathologischen Routinediagnostik des Basalzellkarzinoms: Applying an artificial intelligence deep learning approach to routine dermatopathological diagnosis of basal cell carcinoma. J Dtsch Dermatol Ges 2023; 21:1329-1338. [PMID: 37946648 DOI: 10.1111/ddg.15180_g] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 06/15/2023] [Indexed: 11/12/2023]
Abstract
ZusammenfassungHintergrundDermatopathologische Institute stehen aufgrund immer höherer Anforderungen bei andererseits schwindenden Ressourcen vor zunehmenden Herausforderungen. Basalzellkarzinome stellen einen Großteil des Einsendeguts mit entsprechendem Arbeitsaufwand dar. Gleichzeitig ermöglicht die Digitalisierung von Glasobjektträgern den Einsatz künstlicher Intelligenz (KI)‐basierter Verfahren in der Dermatopathologie. Bislang haben diese Verfahren keinen Einzug in die Routinediagnostik gefunden. Ziel dieser Studie war daher, den Einsatz eines KI‐basierten Modells zur automatisierten Basalzellkarzinom‐Erkennung zu etablieren.Patienten und MethodikIn drei dermatopathologischen Zentren wurden während des täglichen Routinebetriebs Basalzellkarzinom‐Fälle digitalisiert und sowohl klassisch am Mikroskop als auch mittels KI‐basierter Methodik basierend auf neuronalen Netzen mit U‐Net‐Architektur befundet.ErgebnisseIm Routinebetrieb erzielte das Modell eine Sensitivität von 98,23 % und eine Spezifität von 98,51 % (Zentrum 1). Das Modell konnte übergangslos in den anderen Zentren Einsatz finden und erreichte ähnlich hohe Genauigkeiten in der Basalzellkarzinom‐Erkennung (Sensitivität von 97,67 % beziehungsweise 98,57 %, Spezifität von 96,77 % beziehungsweise 98,73 %). Zusätzlich wurden eine automatisierte, KI‐basierte Basalzellkarzinom‐Subtypisierung und Tumordickenmessung etabliert.SchlussfolgerungenKI‐basierte Verfahren können mit einer hohen Genauigkeit im Routinebetrieb Basalzellkarzinome erkennen und signifikant die dermatopathologische Arbeit unterstützen.
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Affiliation(s)
| | | | | | - Klaus Georg Griewank
- Dermatopathologie bei Mainz, Nieder-Olm
- Klinik für Dermatologie, Universitätsklinikum Essen
| | - Eva Hadaschik
- MVZ Dermatopathologie Duisburg Essen GmbH, Essen
- Klinik für Dermatologie, Universitätsklinikum Essen
| | - Sonja Hetzer
- MVZ Dermatopathologie Duisburg Essen GmbH, Essen
| | | | | | - Philipp Jansen
- Klinik und Poliklinik für Dermatologie und Allergologie, Universitätsklinikum Bonn
| | - Peter Maass
- Zentrum für Technomathematik (ZeTeM), Universität Bremen
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Nervil GG, Ternov NK, Vestergaard T, Sølvsten H, Chakera AH, Tolsgaard MG, Hölmich LR. Improving Skin Cancer Diagnostics Through a Mobile App With a Large Interactive Image Repository: Randomized Controlled Trial. JMIR DERMATOLOGY 2023; 6:e48357. [PMID: 37624707 PMCID: PMC10448292 DOI: 10.2196/48357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 07/03/2023] [Indexed: 08/27/2023] Open
Abstract
BACKGROUND Skin cancer diagnostics is challenging, and mastery requires extended periods of dedicated practice. OBJECTIVE The aim of the study was to determine if self-paced pattern recognition training in skin cancer diagnostics with clinical and dermoscopic images of skin lesions using a large-scale interactive image repository (LIIR) with patient cases improves primary care physicians' (PCPs') diagnostic skills and confidence. METHODS A total of 115 PCPs were randomized (allocation ratio 3:1) to receive or not receive self-paced pattern recognition training in skin cancer diagnostics using an LIIR with patient cases through a quiz-based smartphone app during an 8-day period. The participants' ability to diagnose skin cancer was evaluated using a 12-item multiple-choice questionnaire prior to and 8 days after the educational intervention period. Their thoughts on the use of dermoscopy were assessed using a study-specific questionnaire. A learning curve was calculated through the analysis of data from the mobile app. RESULTS On average, participants in the intervention group spent 2 hours 26 minutes quizzing digital patient cases and 41 minutes reading the educational material. They had an average preintervention multiple choice questionnaire score of 52.0% of correct answers, which increased to 66.4% on the postintervention test; a statistically significant improvement of 14.3 percentage points (P<.001; 95% CI 9.8-18.9) with intention-to-treat analysis. Analysis of participants who received the intervention as per protocol (500 patient cases in 8 days) showed an average increase of 16.7 percentage points (P<.001; 95% CI 11.3-22.0) from 53.9% to 70.5%. Their overall ability to correctly recognize malignant lesions in the LIIR patient cases improved over the intervention period by 6.6 percentage points from 67.1% (95% CI 65.2-69.3) to 73.7% (95% CI 72.5-75.0) and their ability to set the correct diagnosis improved by 10.5 percentage points from 42.5% (95% CI 40.2%-44.8%) to 53.0% (95% CI 51.3-54.9). The diagnostic confidence of participants in the intervention group increased on a scale from 1 to 4 by 32.9% from 1.6 to 2.1 (P<.001). Participants in the control group did not increase their postintervention score or their diagnostic confidence during the same period. CONCLUSIONS Self-paced pattern recognition training in skin cancer diagnostics through the use of a digital LIIR with patient cases delivered by a quiz-based mobile app improves the diagnostic accuracy of PCPs. TRIAL REGISTRATION ClinicalTrials.gov NCT05661370; https://classic.clinicaltrials.gov/ct2/show/NCT05661370.
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Affiliation(s)
- Gustav Gede Nervil
- Department of Plastic Surgery, Herlev-Gentofte Hospital, Herlev, Denmark
| | | | - Tine Vestergaard
- Department of Dermatology and Allergy Centre, Odense University Hospital, Odense, Denmark
| | | | | | - Martin Grønnebæk Tolsgaard
- Copenhagen Academy for Medical Education and Simulation, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
- Department of Obstetrics, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Lisbet Rosenkrantz Hölmich
- Department of Plastic Surgery, Herlev-Gentofte Hospital, Herlev, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
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Jansen P, Baguer DO, Duschner N, Arrastia JL, Schmidt M, Landsberg J, Wenzel J, Schadendorf D, Hadaschik E, Maass P, Schaller J, Griewank KG. Deep learning detection of melanoma metastases in lymph nodes. Eur J Cancer 2023; 188:161-170. [PMID: 37257277 DOI: 10.1016/j.ejca.2023.04.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 04/20/2023] [Accepted: 04/25/2023] [Indexed: 06/02/2023]
Abstract
BACKGROUND In melanoma patients, surgical excision of the first draining lymph node, the sentinel lymph node (SLN), is a routine procedure to evaluate lymphogenic metastases. Metastasis detection by histopathological analysis assesses multiple tissue levels with hematoxylin and eosin and immunohistochemically stained glass slides. Considering the amount of tissue to analyze, the detection of metastasis can be highly time-consuming for pathologists. The application of artificial intelligence in the clinical routine has constantly increased over the past few years. METHODS In this multi-center study, a deep learning method was established on histological tissue sections of sentinel lymph nodes collected from the clinical routine. The algorithm was trained to highlight potential melanoma metastases for further review by pathologists, without relying on supplementary immunohistochemical stainings (e.g. anti-S100, anti-MelanA). RESULTS The established method was able to detect the existence of metastasis on individual tissue cuts with an area under the curve of 0.9630 and 0.9856 respectively on two test cohorts from different laboratories. The method was able to accurately identify tumour deposits>0.1 mm and, by automatic tumour diameter measurement, classify these into 0.1 mm to -1.0 mm and>1.0 mm groups, thus identifying and classifying metastasis currently relevant for assessing prognosis and stratifying treatment. CONCLUSIONS Our results demonstrate that AI-based SLN melanoma metastasis detection has great potential and could become a routinely applied aid for pathologists. Our current study focused on assessing established parameters; however, larger future AI-based studies could identify novel biomarkers potentially further improving SLN-based prognostic and therapeutic predictions for affected patients.
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Affiliation(s)
- Philipp Jansen
- Department of Dermatology, University Hospital Bonn, Bonn 53127, Germany
| | | | | | | | | | - Jennifer Landsberg
- Department of Dermatology, University Hospital Bonn, Bonn 53127, Germany
| | - Jörg Wenzel
- Department of Dermatology, University Hospital Bonn, Bonn 53127, Germany
| | - Dirk Schadendorf
- Department of Dermatology, University Hospital Essen, Essen 45147, Germany
| | - Eva Hadaschik
- Department of Dermatology, University Hospital Essen, Essen 45147, Germany
| | | | - Jörg Schaller
- Dermatopathologie Duisburg Essen GmbH, Essen 45329, Germany
| | - Klaus Georg Griewank
- Department of Dermatology, University Hospital Essen, Essen 45147, Germany; Dermatopathologie bei Mainz, Nieder-Olm 55268, Germany.
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Lallas A. Melanoma: update on dermatoscopy, artificial intelligence for diagnosis, histopathology, genetics, surgery and systemic medical treatment. Ital J Dermatol Venerol 2021; 156:271-273. [PMID: 33982544 DOI: 10.23736/s2784-8671.21.06955-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Aimilios Lallas
- School of Medicine, First Department of Dermatology, Faculty of Health Sciences, Aristotle University, Thessaloniki, Greece -
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