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      Teledermoscopy – An Emerging Technology for Skin Cancer Detection

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          Abstract

          Dermatology faces a worsening scarcity of providers, especially since the onset of the COVID-19 pandemic. With lengthening waiting periods for skin cancer screening examinations, there is a distinct need for alternatives to in-person evaluation. Delayed diagnosis is associated with poorer outcomes, especially in melanoma. Teledermatology has the potential to prevent the increased morbidity and mortality associated with late-stage diagnosis, especially when utilized with dermoscopy. In the literature, this novel field of ‘teledermoscopy’ has exhibited accuracy and reliability comparable to face-to-face visits, and it is a promising alternative intervention for those who require triaging or for patients who are unable to access in-person care (rural, underserved populations). Although early data are promising, formal guidelines for acquisition and interpretation of dermatoscopic images must be established before wider implementation is possible. With standardization, use at home or in primary care offices might relieve some of the pressure on an overburdened dermatologic care system and help patients requiring urgent care to be seen more expediently.

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          Most cited references65

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          The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions

          Training of neural networks for automated diagnosis of pigmented skin lesions is hampered by the small size and lack of diversity of available datasets of dermatoscopic images. We tackle this problem by releasing the HAM10000 (“Human Against Machine with 10000 training images”) dataset. We collected dermatoscopic images from different populations acquired and stored by different modalities. Given this diversity we had to apply different acquisition and cleaning methods and developed semi-automatic workflows utilizing specifically trained neural networks. The final dataset consists of 10015 dermatoscopic images which are released as a training set for academic machine learning purposes and are publicly available through the ISIC archive. This benchmark dataset can be used for machine learning and for comparisons with human experts. Cases include a representative collection of all important diagnostic categories in the realm of pigmented lesions. More than 50% of lesions have been confirmed by pathology, while the ground truth for the rest of the cases was either follow-up, expert consensus, or confirmation by in-vivo confocal microscopy.
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            Estimated Projection of US Cancer Incidence and Death to 2040

            Question How will the landscape of cancer incidences and deaths change in the next 2 decades? Findings In this cross-sectional study, the results estimate that leading cancer incidences and deaths in the US will be notably different in the year 2040 compared with current rankings. Estimates included increases in melanoma incidence, pancreatic cancer deaths, and liver cancer deaths, and decreases in prostate cancer incidence and breast cancer deaths. Meaning These estimates will be important to guide research, health care, and health policy efforts and emphasize the importance of cancer screening, early detection, and prevention. This cross-sectional study examines recent data from the Surveillance, Epidemiology, and End Results Program and US Census to estimate projections for US cancer incidence and deaths to 2040. Importance Coping with the current and future burden of cancer requires an in-depth understanding of trends in cancer incidences and deaths. Estimated projections of cancer incidences and deaths will be important to guide future research funding allocations, health care planning, and health policy efforts. Objective To estimate cancer incidences and deaths in the United States to the year 2040. Design and Setting This cross-sectional study’s estimated projection analysis used population growth projections and current population-based cancer incidence and death rates to calculate the changes in incidences and deaths to the year 2040. Cancer-specific incidences and deaths in the US were estimated for the most common cancer types. Demographic cancer-specific delay-adjusted incidence rates from the Surveillance, Epidemiology, and End Results Program were combined with US Census Bureau population growth projections (2016) and average annual percentage changes in incidence and death rates. Statistical analyses were performed from July 2020 to February 2021. Main Outcomes and Measures Total cancer incidences and deaths to the year 2040. Results This study estimated that the most common cancers in 2040 will be breast (364 000 cases) with melanoma (219 000 cases) becoming the second most common cancer; lung, third (208 000 cases); colorectal remaining fourth (147 000 cases); and prostate cancer dropping to the fourteenth most common cancer (66 000 cases). Lung cancer (63 000 deaths) was estimated to continue as the leading cause of cancer-related death in 2040, with pancreatic cancer (46 000 deaths) and liver and intrahepatic bile duct cancer (41 000 deaths) surpassing colorectal cancer (34 000 deaths) to become the second and third most common causes of cancer-related death, respectively. Breast cancer (30 000 deaths) was estimated to decrease to the fifth most common cause of cancer death. Conclusions and Relevance These findings suggest that there will be marked changes in the landscape of cancer incidence and deaths by 2040.
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              Global Burden of Cutaneous Melanoma in 2020 and Projections to 2040

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                Author and article information

                Journal
                THMT
                Telehealth and Medicine Today
                Partners in Digital Health
                2471-6960
                29 December 2022
                2022
                : 7
                : 10.30953/thmt.v8.376
                Affiliations
                [1 ]Virginia Commonwealth University School of Medicine, Richmond, Virginia, USA
                [2 ]Skinvest, Inc., Chapel Hill, North Carolina, USA
                [3 ]Dermatology and Cutaneous Surgery, University of South Florida Health Morsani College of Medicine, Tampa, Florida, USA
                [4 ]Department of Dermatology, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
                [5 ]Department of Family Medicine, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
                Author notes
                [* ]Correspondence: Kristen Delans Email: lospinosokr@ 123456vcu.edu
                Author information
                https://orcid.org/0000-0003-1458-0723
                https://orcid.org/0000-0002-9333-3116
                https://orcid.org/0000-0002-9185-0056
                https://orcid.org/0000-0003-2436-3917
                https://orcid.org/0000-0002-0664-235X
                Article
                376
                10.30953/thmt.v8.376
                445acf34-d77a-4292-8587-028ccf808ff7
                © 2022 Kristen Delans et al.

                This is an open access article distributed in accordance with the Creative Commons Attribution Non-Commercial (CC BY-NC 4.0) license, which permits others to distribute, adapt, enhance this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial.

                History
                : 22 October 2022
                : 27 November 2022
                Categories
                REVIEW

                Social & Information networks,General medicine,General life sciences,Health & Social care,Public health,Hardware architecture
                dermoscopy,dermatoscopy,smartphone teledermatology,melanoma,skin cancer,telehealth,teledermoscopy

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