Mole Mapping for Management of Pigmented Skin Lesions

Not Your Mother's Melanoma: Causes and Effects of Early Melanoma Diagnosis

The year 2022 will herald approximately 100,000 new cases of cutaneous melanoma (CM), and over 7000 deaths from CM. Over the past 40 years, CM incidence has increased nearly six-fold; however, annual mortality has remained relatively constant. These trends encapsulate the phenomenon of overdiagnosis. Increased recognition of indolent lesions that appear histologically malignant may be leading to a melanoma epidemic. Enhanced melanoma awareness, screening efforts, physician uncertainty, medical-legal pressures, and diagnostic scrutiny using tools like immunohistochemical staining, mole mapping, dermoscopy, confocal microscopy, and molecular diagnostics contribute to increased CM diagnosis. As a result, current melanoma staging and treatment guidelines are being challenged. Existing standards fail to accurately identify histologically benign lesions that are lethal or, conversely, histologically malignant lesions that are innocuous. Healthcare systems and, more importantly, patients suffer from this diagnostic ambiguity that leads to the over-treatment of innocuous melanomas and under-treatment of aggressive melanomas. As dermatology continues to experience a shift towards earlier diagnosis of melanoma, management strategies must adapt. Herein, we review factors that may contribute to the increased incidence of melanoma, emphasize deficiencies in current staging systems, and provide insights into the future of melanoma management via precision medicine.

Artificial Intelligence in Dermatology Image Analysis: Current Developments and Future Trends

BACKGROUND

Thanks to the rapid development of computer-based systems and deep-learning-based algorithms, artificial intelligence (AI) has long been integrated into the healthcare field. AI is also particularly helpful in image recognition, surgical assistance and basic research. Due to the unique nature of dermatology, AI-aided dermatological diagnosis based on image recognition has become a modern focus and future trend. Key scientific concepts of review: The use of 3D imaging systems allows clinicians to screen and label skin pigmented lesions and distributed disorders, which can provide an objective assessment and image documentation of lesion sites. Dermatoscopes combined with intelligent software help the dermatologist to easily correlate each close-up image with the corresponding marked lesion in the 3D body map. In addition, AI in the field of prosthetics can assist in the rehabilitation of patients and help to restore limb function after amputation in patients with skin tumors.

THE AIM OF THE STUDY

For the benefit of patients, dermatologists have an obligation to explore the opportunities, risks and limitations of AI applications. This study focuses on the application of emerging AI in dermatology to aid clinical diagnosis and treatment, analyzes the current state of the field and summarizes its future trends and prospects so as to help dermatologists realize the impact of new technological innovations on traditional practices so that they can embrace and use AI-based medical approaches more quickly.

Virtual lesion clinic - Evaluation of a teledermatology triage system for referrals for suspected melanoma

INTRODUCTION

The Virtual Lesion Clinic (VLC) of Waitematā District Health Board (WDHB) improves melanoma assessment and treatment using teledermatology. The VLC is reserved for pigmented lesions referred as suspected melanoma from primary care but indeterminate at the initial triage.

OBJECTIVES

To assess the efficacy of the VLC diagnosis of melanoma.

METHODS

A retrospective audit of suspected melanoma referrals (1 January 2012 to 31 December 2016) was conducted. Lesions were referred to the VLC if diagnostic uncertainty remained at the initial triage. VLC patients attended MoleMap imaging centres, a dermatologist reviewed history and images remotely and suggested a diagnosis and management plan. Post VLC provisional diagnosis of melanoma, all lesions subsequently excised were reviewed. A positive predictive value (PPV) was calculated using concordance between VLC diagnosis of melanoma and histopathological diagnosis of melanoma. Number needed to excise (NNE) for melanoma was derived, as well as an invasive to in-situ melanoma ratio (IM:MIS) and false negative rate (FNR).

RESULTS

The VLC received 1874 referrals for 3546 lesions during the 5-year study period. Six hundred and seventy-nine lesions were recommended excision/biopsy or specialist face-to-face assessment, and 504 lesions were subsequently excised. The PPV was 62%, NNE 1.62 and IM:MIS 0.76 for lesions suspected to be melanoma at VLC assessment. The VLC had a melanoma-specific FNR of 7%.

CONCLUSIONS

The VLC is effective in early diagnosis of melanoma with a high positive predictive value, low number needed to excise and low false negative rate amongst lesions referred as suspected melanoma.

New diagnostic and imaging technologies in dermatology

INTRODUCTION

Diagnosis of dermatological disorders is primarily based on clinical examination in combination with histopathology. However, clinical findings alone may not be sufficient for accurate diagnosis and cutaneous biopsies are being associated with morbidity.

OBJECTIVE

The objective of this article is to review the newer technologies along with their applications, limitation and future prospectus.

METHODOLOGY

Comprehensive literature search was performed using electronic online databases "PubMed" and "Google Scholar". Articles published in English language were considered for the review.

RESULTS

In order to improve and/or widen the armamentarium in dermatologic disease diagnosis and therapy, newer emerging technologies are being made available which aid in diagnosis and management. New emerging technologies include confocal microscopy, digital photographic imaging, optical coherence tomography, high frequency ultrasonography, and artificial intelligence. There have been advancements in the dermoscopes.

CONCLUSION

Significant progress is seen in the diagnostic methods and imaging technologies in dermatology, each having its advantages and limitations. Artificial intelligence/machine-based learning software may have a great scope to influence the dermatological practice.

Accessing Mole-Scanning through Community Pharmacy: A Pilot Service in Collaboration with Dermatology Specialists

Early identification and treatment of malignant melanoma is crucial to prevent mortality. The aim of this work was to describe the uptake, profile of users and service outcomes of a mole scanning service in the community pharmacy setting in the UK. In addition, health care costs saved from the perspective of general practice were estimated. The service allowed patients to have concerning skin lesions scanned with a dermatoscopy device which were analyzed remotely by clinical dermatology specialists in order to provide recommendations for the patient. Patients were followed up to ascertain the clinical outcome. Data were analyzed for 6355 patients and 9881 scans across 50 community pharmacies. The majority of the scans required no further follow-up (n = 8763, 88.7%). Diagnosis was confirmed for 70.4% (n = 757/1118) of scans where patients were recommended to seek further medical attention. Of these, 44.3% were ultimately defined as normal (n = 335) and 6.2% as malignant melanoma (n = 47/757). An estimated 0.7% of scans taken as part of the service led to a confirmed diagnosis of malignant melanoma. This service evaluation has shown that a mole scanning service available within community pharmacies is effective at triaging patients and ultimately playing a part in identifying diagnoses of malignant melanoma.

LesionMap: A Method and Tool for the Semantic Annotation of Dermatological Lesions for Documentation and Machine Learning

Diagnosis and follow-up of patients in dermatology rely on visual cues. Documentation of skin lesions in dermatology is time-consuming and inaccurate. Digital photography is resource-intensive, difficult to standardize, and has privacy concerns. We propose a simple method—LesionMap—and an electronic health software tool—LesionMapper—for semantically annotating dermatological lesions on a body wireframe. We discuss how the type, distribution, and progression of lesions can be represented in a standardized way. The tool is an open-source JavaScript package that can be integrated into web-based electronic medical records. We believe that LesionMapper will facilitate documentation in dermatology that can be used for machine learning in a privacy-preserving manner.

[Artificial intelligence in plastic surgery : Current developments and perspectives]

BACKGROUND

Artificial intelligence (AI) has long been established in various parts of everyday life due to the instrumentalization of machines and robotics in industry, autonomous vehicles and the rapid development of computer-based systems.

OBJECTIVE

Demonstration of current developments and perspectives of AI in plastic surgery.

MATERIAL AND METHODS

Evaluation of statistics, press releases and original articles from journals and discussion of reviews.

RESULTS

In the healthcare system and in plastic surgery AI is particularly useful in the context of data analysis from digital patient files and big data from central registers. The use of 3D imaging systems provides objective feedback on surgical results in terms of volume and aesthetics. Intelligent robots assist plastic surgeons in microsurgical anastomoses of increasingly smaller vessels and the implementation of AI in the field of prosthetics enables patients to regain hand function following amputation injuries.

CONCLUSION

For the benefit of the patients, it is the responsibility of experimental surgery to explore the opportunities, risks and limitations of applications with AI.

Emerging imaging technologies in dermatology: Part II: Applications and limitations

Clinical examination is critical for the diagnosis and identification of response to treatment. It is fortunate that technologies are continuing to evolve, enabling augmentation of classical clinical examination with noninvasive imaging modalities. This article discusses emerging technologies with a focus on digital photographic imaging, confocal microscopy, optical coherence tomography, and high-frequency ultrasound, as well as several additional developing modalities. The most readily adopted technologies to date include total-body digital photography and dermoscopy, with some practitioners beginning to use confocal microscopy. In this article, applications and limitations are addressed. For a detailed discussion of the principles involved in these technologies, please refer to the first part of this review article.

Emerging imaging technologies in dermatology: Part I: Basic principles

Dermatologists rely primarily on clinical examination in combination with histopathology to diagnose conditions; however, clinical examination alone might not be sufficient for accurate diagnosis and skin biopsies have associated morbidity. With continued technological advancement, there are emerging ancillary imaging technologies available to dermatologists to aid in diagnosis and management. This 2-part review article will discuss these emerging technologies including: digital photographic imaging, confocal microscopy, optical coherence tomography, and high-frequency ultrasound, as well as several additional modalities in development. In this first installment, the authors describe the breadth of technologies available and the science behind them. Then, in the second article, the authors discuss the applications and limitations of these technologies and future directions.