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Article ; Online: The Role of DICOM in Artificial Intelligence for Skin Disease

Liam J. Caffery / Veronica Rotemberg / Jochen Weber / H. Peter Soyer / Josep Malvehy / David Clunie

Frontiers in Medicine, Vol

2021 Volume 7

Abstract: There is optimism that artificial intelligence (AI) will result in positive clinical outcomes, which is driving research and investment in the use of AI for skin disease. At present, AI for skin disease is embedded in research and development and not ... ...

Abstract	There is optimism that artificial intelligence (AI) will result in positive clinical outcomes, which is driving research and investment in the use of AI for skin disease. At present, AI for skin disease is embedded in research and development and not practiced widely in clinical dermatology. Clinical dermatology is also undergoing a technological transformation in terms of the development and adoption of standards that optimizes the quality use of imaging. Digital Imaging and Communications in Medicine (DICOM) is the international standard for medical imaging. DICOM is a continually evolving standard. There is considerable effort being invested in developing dermatology-specific extensions to the DICOM standard. The ability to encode relevant metadata and afford interoperability with the digital health ecosystem (e.g., image repositories, electronic medical records) has driven the initial impetus in the adoption of DICOM for dermatology. DICOM has a dedicated working group whose role is to develop a mechanism to support AI workflows and encode AI artifacts. DICOM can improve AI workflows by encoding derived objects (e.g., secondary images, visual explainability maps, AI algorithm output) and the efficient curation of multi-institutional datasets for machine learning training, testing, and validation. This can be achieved using DICOM mechanisms such as standardized image formats and metadata, metadata-based image retrieval, and de-identification protocols. DICOM can address several important technological and workflow challenges for the implementation of AI. However, many other technological, ethical, regulatory, medicolegal, and workforce barriers will need to be addressed before DICOM and AI can be used effectively in dermatology.
Keywords	dermatology ; artificial intelligence ; DICOM ; standards ; imaging ; Medicine (General) ; R5-920
Subject code	020
Language	English
Publishing date	2021-02-01T00:00:00Z
Publisher	Frontiers Media S.A.
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: On Naevi and Melanomas

Katie J. Lee / Monika Janda / Mitchell S. Stark / Richard A. Sturm / H. Peter Soyer

Frontiers in Medicine, Vol

Two Sides of the Same Coin?

2021 Volume 8

Abstract: Benign naevi are closely linked to melanoma, as risk factors, simulators, or sites of melanoma formation. There is a heavy genetic overlap between the two lesions, a shared environmental influence of ultraviolet radiation, and many similar cellular ... ...

Abstract	Benign naevi are closely linked to melanoma, as risk factors, simulators, or sites of melanoma formation. There is a heavy genetic overlap between the two lesions, a shared environmental influence of ultraviolet radiation, and many similar cellular features, yet naevi remain locally situated while melanomas spread from their primary site and may progress systemically to distal organs. Untangling the overlapping contributors and predictors of naevi and melanoma is an ongoing area of research and should eventually lead to more personalized prevention and treatment strategies, through the development of melanoma risk stratification tools and early detection of evolving melanomas. This will be achieved through a range of complementary strategies: risk-adjusted primary prevention counseling; the use of lesion imaging technologies such as sequential 3D total body photography and consumer-performed lesion imaging; artificial intelligence deep phenotyping and clinical assistance; a better understanding of genetic drivers of malignancy, risk variants, clinical genetics, and polygenic effects; and the interplay between genetics, phenotype and the environment.
Keywords	precancer ; precursor lesion ; genetics and genomics ; artificial intelligence ; risk stratification ; melanoma ; Medicine (General) ; R5-920
Subject code	610
Language	English
Publishing date	2021-02-01T00:00:00Z
Publisher	Frontiers Media S.A.
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Editorial

Chengxu Li / Je-Ho Mun / Paola Pasquali / Hang Li / H. Peter Soyer / Yong Cui

Frontiers in Medicine, Vol

Progress and Prospects on Skin Imaging Technology, Teledermatology and Artificial Intelligence in Dermatology

2021 Volume 8

Keywords	skin imaging technology ; dermoscopy ; reflectance confocal microscopy ; teledermatology ; artificial intelligence in dermatology ; big data in dermatology ; Medicine (General) ; R5-920
Language	English
Publishing date	2021-11-01T00:00:00Z
Publisher	Frontiers Media S.A.
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Protocol to evaluate a pilot program to upskill clinicians in providing genetic testing for familial melanoma

Clare A. Primiero / Anna Finnane / Tatiane Yanes / Betsy Peach / H. Peter Soyer / Aideen M. McInerney-Leo

PLoS ONE, Vol 17, Iss

2022 Volume 12

Abstract: Introduction Genetic testing for hereditary cancers can improve long-term health outcomes through identifying high-risk individuals and facilitating targeted prevention and screening/surveillance. The rising demand for genetic testing exceeds the ... ...

Abstract	Introduction Genetic testing for hereditary cancers can improve long-term health outcomes through identifying high-risk individuals and facilitating targeted prevention and screening/surveillance. The rising demand for genetic testing exceeds the clinical genetic workforce capacity. Therefore, non-genetic specialists need to be empowered to offer genetic testing. However, it is unknown whether patient outcomes differ depending on whether genetic testing is offered by a genetics specialist or a trained non-genetics clinician. This paper describes a protocol for upskilling non-genetics clinicians to provide genetic testing, randomise high-risk individuals to receive testing from a trained clinician or a genetic counsellor, and then determine whether patient outcomes differed depending on provider-type. Methods An experiential training program to upskill dermatologically-trained clinicians to offer genetic testing for familial melanoma is being piloted on 10–15 clinicians, prior to wider implementation. Training involves a workshop, comprised of a didactic learning presentation, case studies, simulated sessions, and provision of supporting documentation. Clinicians later observe a genetic counsellor led consultation before being observed leading a consultation. Both sessions are followed by debriefing with a genetic counsellor. Thereafter, clinicians independently offer genetic testing in the clinical trial. Individuals with a strong personal and/or family history of melanoma are recruited to a parallel-group trial and allocated to receive pre- and post- genetic testing consultation from a genetic counsellor, or a dermatologically-trained clinician. A mixed method approach measures psychosocial and behavioural outcomes. Longitudinal online surveys are administered at five timepoints from baseline to one year post-test disclosure. Semi-structured interviews with both patients and clinicians are qualitatively analysed. Significance This is the first program to upskill dermatologically-trained clinicians to provide ...
Keywords	Medicine ; R ; Science ; Q
Subject code	170
Language	English
Publishing date	2022-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: The Impact of SARS-CoV-2 (COVID-19) Pandemic on International Dermatology Conferences in 2020

Eun Seo Ha / Ji Yeon Hong / Sophie Soyeon Lim / H. Peter Soyer / Je-Ho Mun

Frontiers in Medicine, Vol

2021 Volume 8

Abstract: To limit the spread of the SARS-CoV-2 (COVID-19) outbreak, humans have been significantly restricted in their ability to travel and interact with others worldwide. Consequently, dermatology conferences were forced to adapt to such changes. The aim of ... ...

Abstract	To limit the spread of the SARS-CoV-2 (COVID-19) outbreak, humans have been significantly restricted in their ability to travel and interact with others worldwide. Consequently, dermatology conferences were forced to adapt to such changes. The aim of this study is to investigate the impact of COVID-19 on international dermatology conferences. We retrospectively investigated decisions made for international dermatology conferences scheduled for 2020. Thirty-three major conferences were analyzed. Their data were obtained from their respective websites (data was accessed 2 June 2021). Among 33 conferences analyzed, 13 (39.4%) were conducted as scheduled, nine (27.3%) were canceled, eight (24.3%) were postponed to 2021 or 2022, and three (9.1%) were delayed but conducted in 2020. The number of the cancellation (44.4%) and postponement (75%) was the largest in the second quarter of the year. During the fourth quarter, most conferences were held on schedule (70%) but were run virtually. Eight out of 13 virtual conferences shortened their duration (61.5%). Most (90.9%) conferences have decided on the schedule of their meetings for 2021 or 2022 while three (9.1%) remain undecided. Twelve (40%) are planned to run virtually, eight (26.7%) have opted for a hybrid form, five (16.7%) are planned to run in-person, four (13.3%) have not decided on the format, and one (3.3%) has been canceled. Virtual and hybrid conference formats have facilitated people to share knowledge despite the travel restrictions posed by the COVID-19 pandemic. Such formats are environmentally friendly, are able to attract a large audience, and save delegates time and costs involved in attending. Therefore, virtual platforms should continue to be integrated within conferences in the post-pandemic era.
Keywords	COVID-19 ; conference ; virtual ; dermatology ; coronavirus ; pandemics ; Medicine (General) ; R5-920
Language	English
Publishing date	2021-08-01T00:00:00Z
Publisher	Frontiers Media S.A.
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: The Future of Precision Prevention for Advanced Melanoma

Katie J. Lee / Brigid Betz-Stablein / Mitchell S. Stark / Monika Janda / Aideen M. McInerney-Leo / Liam J. Caffery / Nicole Gillespie / Tatiane Yanes / H. Peter Soyer

Frontiers in Medicine, Vol

2022 Volume 8

Abstract: Precision prevention of advanced melanoma is fast becoming a realistic prospect, with personalized, holistic risk stratification allowing patients to be directed to an appropriate level of surveillance, ranging from skin self-examinations to regular ... ...

Abstract	Precision prevention of advanced melanoma is fast becoming a realistic prospect, with personalized, holistic risk stratification allowing patients to be directed to an appropriate level of surveillance, ranging from skin self-examinations to regular total body photography with sequential digital dermoscopic imaging. This approach aims to address both underdiagnosis (a missed or delayed melanoma diagnosis) and overdiagnosis (the diagnosis and treatment of indolent lesions that would not have caused a problem). Holistic risk stratification considers several types of melanoma risk factors: clinical phenotype, comprehensive imaging-based phenotype, familial and polygenic risks. Artificial intelligence computer-aided diagnostics combines these risk factors to produce a personalized risk score, and can also assist in assessing the digital and molecular markers of individual lesions. However, to ensure uptake and efficient use of AI systems, researchers will need to carefully consider how best to incorporate privacy and standardization requirements, and above all address consumer trust concerns.
Keywords	melanoma ; prevention ; artificial intelligence ; genomics ; risk stratification ; Medicine (General) ; R5-920
Subject code	610
Language	English
Publishing date	2022-01-01T00:00:00Z
Publisher	Frontiers Media S.A.
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A survey of clinicians on the use of artificial intelligence in ophthalmology, dermatology, radiology and radiation oncology

Jane Scheetz / Philip Rothschild / Myra McGuinness / Xavier Hadoux / H. Peter Soyer / Monika Janda / James J.J. Condon / Luke Oakden-Rayner / Lyle J. Palmer / Stuart Keel / Peter van Wijngaarden

Scientific Reports, Vol 11, Iss 1, Pp 1-

2021 Volume 10

Abstract: Abstract Artificial intelligence technology has advanced rapidly in recent years and has the potential to improve healthcare outcomes. However, technology uptake will be largely driven by clinicians, and there is a paucity of data regarding the attitude ... ...

Abstract	Abstract Artificial intelligence technology has advanced rapidly in recent years and has the potential to improve healthcare outcomes. However, technology uptake will be largely driven by clinicians, and there is a paucity of data regarding the attitude that clinicians have to this new technology. In June–August 2019 we conducted an online survey of fellows and trainees of three specialty colleges (ophthalmology, radiology/radiation oncology, dermatology) in Australia and New Zealand on artificial intelligence. There were 632 complete responses (n = 305, 230, and 97, respectively), equating to a response rate of 20.4%, 5.1%, and 13.2% for the above colleges, respectively. The majority (n = 449, 71.0%) believed artificial intelligence would improve their field of medicine, and that medical workforce needs would be impacted by the technology within the next decade (n = 542, 85.8%). Improved disease screening and streamlining of monotonous tasks were identified as key benefits of artificial intelligence. The divestment of healthcare to technology companies and medical liability implications were the greatest concerns. Education was identified as a priority to prepare clinicians for the implementation of artificial intelligence in healthcare. This survey highlights parallels between the perceptions of different clinician groups in Australia and New Zealand about artificial intelligence in medicine. Artificial intelligence was recognized as valuable technology that will have wide-ranging impacts on healthcare.
Keywords	Medicine ; R ; Science ; Q
Subject code	401
Language	English
Publishing date	2021-03-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Study protocol for a randomised controlled trial to evaluate the use of melanoma surveillance photography to the Improve early detection of MelanomA in ultra-hiGh and high-risk patiEnts (the IMAGE trial)

Mabel K. Yan / Anne E. Cust / H. Peter Soyer / Monika Janda / Katja Loewe / Gabrielle Byars / Paul Fishburn / Paul White / Rashidul Alam Mahumud / Robyn P. M. Saw / Alan Herschtal / Pablo Fernandez-Penas / Pascale Guitera / Rachael L. Morton / John Kelly / Rory Wolfe / Victoria J. Mar

Trials, Vol 24, Iss 1, Pp 1-

2023 Volume 14

Abstract: Abstract Introduction Melanoma surveillance photography (MSP) is a comprehensive surveillance method that comprises two- or three-dimensional total body photography with tagged digital dermoscopy, performed at prescribed intervals. It has the potential ... ...

Abstract	Abstract Introduction Melanoma surveillance photography (MSP) is a comprehensive surveillance method that comprises two- or three-dimensional total body photography with tagged digital dermoscopy, performed at prescribed intervals. It has the potential to reduce unnecessary biopsies and enhance early detection of melanoma, but it is not yet standard care for all high-risk patients in Australia. This protocol describes a randomised controlled trial (RCT) designed to evaluate the clinical impact and cost-effectiveness of using MSP for the surveillance of individuals at ultra-high or high risk of melanoma from a health system perspective. Methods and design This is a registry-based, unblinded, multi-site, parallel-arm RCT that will be conducted over 3 years. We aim to recruit 580 participants from three Australian states: Victoria, New South Wales and Queensland, via state cancer registries or direct referral from clinicians. Eligible participants within 24 months of a primary cutaneous melanoma diagnosis will be randomised 1:1 to receive either MSP in addition to their routine clinical surveillance (intervention group) or routine clinical surveillance without MSP (control group). Most participants will continue surveillance with their usual care provider, and the frequency of follow-up visits in both groups will depend on the stage of their primary melanoma and risk factors. The primary outcome measure of the study is the number of unnecessary biopsies (i.e. false positives, being cases where a lesion is biopsied due to suspected melanoma on clinical examination, either with or without MSP, but the resulting histopathology finding is negative for melanoma). Secondary outcomes include the evaluation of health economic outcomes, quality of life and patient acceptability. Two sub-studies will explore the benefit of MSP in high-risk patients prior to a melanoma diagnosis and the diagnostic performance of MSP in the teledermatology setting compared to the en face clinical setting. Discussion This trial will determine ...
Keywords	Total body photography ; Melanoma ; Surveillance ; Early detection ; Secondary prevention ; Cost–benefit analysis ; Medicine (General) ; R5-920
Subject code	610
Language	English
Publishing date	2023-03-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Developing an Australian Melanoma Clinical Outcomes Registry (MelCOR)

John Zalcberg / Sue Evans / H Peter Soyer / Mark Shackleton / Graham Mann / Victoria Mar / Dale Jobson / Benjamin Roffey / Renee Best / Alison Button-Sloan / Danica Cossio / Catherine Shang / Julie Moore / Christopher Arnold / Rachel L Morton

BMJ Open, Vol 12, Iss

a protocol paper

2022 Volume 9

Abstract: Introduction Australia has the highest incidence of melanoma in the world with variable care provided by a diverse range of clinicians. Clinical quality registries aim to identify these variations in care and provide anonymised, benchmarked feedback to ... ...

Abstract	Introduction Australia has the highest incidence of melanoma in the world with variable care provided by a diverse range of clinicians. Clinical quality registries aim to identify these variations in care and provide anonymised, benchmarked feedback to clinicians and institutions to improve patient outcomes. The Australian Melanoma Clinical Outcomes Registry (MelCOR) aims to collect population-wide, clinical-level data for the early management of cutaneous melanoma and provide anonymised feedback to healthcare providers.Methods and analysis A modified Delphi process will be undertaken to identify key clinical quality indicators for inclusion in the MelCOR pilot. MelCOR will prospectively collect data relevant to these quality indicators, initially for all people over the age of 18 years living in Victoria and Queensland with a melanoma diagnosis confirmed by histopathology, via a two-stage recruitment and consent process. In stage 1, existing State-based cancer registries contact the treating clinician and provide an opportunity for them to opt themselves or their patients out of direct contact with MelCOR. After stage 1, re-identifiable clinical data are provided to the MelCOR under a waiver of consent. In stage 2, the State-based cancer registry will approach the patient directly and invite them to opt in to MelCOR and share identifiable data. If a patient elects to opt in, MelCOR will be able to contact patients directly to collect patient-reported outcome measures. Aggregated data will be used to provide benchmarked, comparative feedback to participating institutions/clinicians.Ethics and dissemination Following the successful collection of pilot data, the feasibility of an Australia-wide roll out will be evaluated. Key quality indicator data will be the core of the MelCOR dataset, with additional data points added later. Annual reports will be issued, first to the relevant stakeholders followed by the public. MelCOR is approved by the Alfred Ethics Committee (58280/127/20).
Keywords	Medicine ; R
Subject code	170
Language	English
Publishing date	2022-09-01T00:00:00Z
Publisher	BMJ Publishing Group
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Automated Segmentation of Skin Strata in Reflectance Confocal Microscopy Depth Stacks.

Samuel C Hames / Marco Ardigò / H Peter Soyer / Andrew P Bradley / Tarl W Prow

PLoS ONE, Vol 11, Iss 4, p e

2016 Volume 0153208

Abstract: Reflectance confocal microscopy (RCM) is a powerful tool for in-vivo examination of a variety of skin diseases. However, current use of RCM depends on qualitative examination by a human expert to look for specific features in the different strata of the ... ...

Abstract	Reflectance confocal microscopy (RCM) is a powerful tool for in-vivo examination of a variety of skin diseases. However, current use of RCM depends on qualitative examination by a human expert to look for specific features in the different strata of the skin. Developing approaches to quantify features in RCM imagery requires an automated understanding of what anatomical strata is present in a given en-face section. This work presents an automated approach using a bag of features approach to represent en-face sections and a logistic regression classifier to classify sections into one of four classes (stratum corneum, viable epidermis, dermal-epidermal junction and papillary dermis). This approach was developed and tested using a dataset of 308 depth stacks from 54 volunteers in two age groups (20-30 and 50-70 years of age). The classification accuracy on the test set was 85.6%. The mean absolute error in determining the interface depth for each of the stratum corneum/viable epidermis, viable epidermis/dermal-epidermal junction and dermal-epidermal junction/papillary dermis interfaces were 3.1 μm, 6.0 μm and 5.5 μm respectively. The probabilities predicted by the classifier in the test set showed that the classifier learned an effective model of the anatomy of human skin.
Keywords	Medicine ; R ; Science ; Q
Subject code	571
Language	English
Publishing date	2016-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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