An artificial intelligence based app for skin cancer detection evaluated in a population based setting
Schreuder, K., de Groot, J., Hollestein, L. M., Louwman, M. huidkankerrapport IKNL, (2019).
Tokez, S., Hollestein, L., Louwman, M., Nijsten, T. & Wakkee, M. Incidence of multiple vs first cutaneous squamous cell carcinoma on a nationwide scale and estimation of future incidences of cutaneous squamous cell carcinoma. JAMA Dermatol. 156, 1300–1306 (2020).
Google Scholar
Lomas, A., Leonardi-Bee, J. & Bath-Hextall, F. A systematic review of worldwide incidence of nonmelanoma skin cancer. Br. J. Dermatol. 166, 1069–1080 (2012).
Google Scholar
Chen, S. T., Geller, A. C. & Tsao, H. Update on the epidemiology of melanoma. Curr. Dermatol. Rep. 2, 24–34 (2013).
Google Scholar
Janda, M. & Soyer, H. P. Can clinical decision making be enhanced by artificial intelligence? Br. J. Dermatol. 180, 247–248 (2019).
Google Scholar
Tschandl, P. et al. Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study. Lancet Oncol. 20, 938–947 (2019).
Google Scholar
Esteva, A. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115–118 (2017).
Google Scholar
Haenssle, H. A. et al. Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Ann. Oncol. 29, 1836–1842 (2018).
Google Scholar
Freeman, K. et al. Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies. BMJ 368, m127 (2020).
Google Scholar
CZ. SkinVision reimbursement CZ, (2023).
Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. AI in health and medicine. Nat. Med. 28, 31–38 (2022).
Google Scholar
Udrea, A. et al. Accuracy of a smartphone application for triage of skin lesions based on machine learning algorithms. J. Eur. Acad. Dermatol. Venereol. 34, 648–655 (2020).
Google Scholar
Sangers, T. et al. Validation of a market-approved artificial intelligence mobile health app for skin cancer screening: a prospective multicenter diagnostic accuracy study. Dermatology, 1–8, (2022).
Taksler, G. B., Keating, N. L. & Rothberg, M. B. Implications of false-positive results for future cancer screenings. Cancer 124, 2390–2398 (2018).
Google Scholar
Nelson, K. C., Swetter, S. M., Saboda, K., Chen, S. C. & Curiel-Lewandrowski, C. Evaluation of the number-needed-to-biopsy metric for the diagnosis of cutaneous melanoma: a systematic review and meta-analysis. JAMA Dermatol. 155, 1167–1174 (2019).
Google Scholar
Johansson, M., Brodersen, J., Gotzsche, P. C. & Jorgensen, K. J. Screening for reducing morbidity and mortality in malignant melanoma. Cochrane Database Syst. Rev. 6, CD012352 (2019).
Google Scholar
Adamson, A. S., Suarez, E. A. & Welch, H. G. Estimating overdiagnosis of melanoma using trends among black and white patients in the US. JAMA Dermatol. (2022).
Boniol, M., Autier, P. & Gandini, S. Melanoma mortality following skin cancer screening in Germany. BMJ Open 5, e008158 (2015).
Google Scholar
Stang, A. & Jockel, K. H. Does skin cancer screening save lives? A detailed analysis of mortality time trends in Schleswig-Holstein and Germany. Cancer 122, 432–437 (2016).
Google Scholar
Welch, H. G., Mazer, B. L. & Adamson, A. S. The rapid rise in cutaneous melanoma diagnoses. N. Engl. J. Med. 384, 72–79 (2021).
Google Scholar
Adamson, A. S. & Welch, H. G. Machine learning and the cancer-diagnosis problem – no gold standard. N. Engl. J. Med. 381, 2285–2287 (2019).
Google Scholar
Sangers, T. E., Nijsten, T. & Wakkee, M. Mobile health skin cancer risk assessment campaign using artificial intelligence on a population-wide scale: a retrospective cohort analysis. J. Eur. Acad. Dermatol. Venereol. 35, e772–e774 (2021).
Google Scholar
Urban, K., Mehrmal, S., Uppal, P., Giesey, R. L. & Delost, G. R. The global burden of skin cancer: a longitudinal analysis from the Global Burden of Disease Study, 1990-2017. JAAD Int. 2, 98–108 (2021).
Google Scholar
Kulkarni, R. P., Yu, W. Y. & Leachman, S. A. To improve melanoma outcomes, focus on risk stratification, not overdiagnosis. JAMA Dermatol. (2022).
Gordon, L. G. & Rowell, D. Health system costs of skin cancer and cost-effectiveness of skin cancer prevention and screening: a systematic review. Eur. J. Cancer Prev. 24, 141–149 (2015).
Google Scholar
Gordon, L. G. et al. Cost-effectiveness analysis of a skin awareness intervention for early detection of skin cancer targeting men older than 50 years. Value Health 20, 593–601 (2017).
Google Scholar
Matsumoto, M. et al. Estimating the cost of skin cancer detection by dermatology providers in a large health care system. J. Am. Acad. Dermatol. 78, 701–709 (2018).
Google Scholar
Barlow, W. E. et al. Evaluating screening participation, follow-up, and outcomes for breast, cervical, and colorectal cancer in the PROSPR Consortium. J. Natl Cancer Inst. 112, 238–246 (2020).
Google Scholar
Borrelli, C. et al. NHS Breast Screening Programme (NHS, 2016).
Knudsen, A. B. et al. Estimation of benefits, burden, and harms of colorectal cancer screening strategies: modeling study for the US Preventive Services Task Force. JAMA 315, 2595–2609 (2016).
Google Scholar
Siu, A. L. U.S. Preventive Services Task Force Screening for Breast Cancer: U.S. Preventive Services Task Force Recommendation Statement. Ann. Intern. Med. 164, 279–296 (2016).
Google Scholar
Jansen, E. et al. Cost-effectiveness of HPV-based cervical screening based on first year results in the Netherlands: a modelling study. BJOG 128, 573–582 (2021).
Google Scholar
Mandrik, O. et al. Systematic reviews as a “lens of evidence”: determinants of cost-effectiveness of breast cancer screening. Cancer Med. 8, 7846–7858 (2019).
Google Scholar
Ran, T. et al. Cost-effectiveness of colorectal cancer screening strategies-a systematic review. Clin. Gastroenterol. Hepatol. 17, 1969–1981 (2019).
Google Scholar
Global Burden of Disease 2019 Cancer Collaboration et al. Cancer incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life years for 29 cancer groups from 2010 to 2019: a systematic analysis for the global burden of disease study 2019. JAMA Oncol. 8, 420–444 (2022).
Pil, L. et al. Cost-effectiveness and budget effect analysis of a population-based skin cancer screening. JAMA Dermatol. 153, 147–153 (2017).
Google Scholar
Anand, N., Edwards, L., Baker, L. X., Chren, M. M. & Wheless, L. Validity of using billing codes from electronic health records to estimate skin cancer counts. JAMA Dermatol. 157, 1089–1094 (2021).
Google Scholar
Pearce, N. Analysis of matched case-control studies. BMJ 352, i969 (2016).
Google Scholar
Buisman, L. R. et al. The early bird catches the worm: early cost-effectiveness analysis of new medical tests. Int. J. Technol. Assess Health Care 32, 46–53 (2016).
Google Scholar
Register, N. T. Trial NL9586. The impact of a SmartPhone applicatiOn for skin cancer risk assessmenT on the healthcare system (SPOT-study): a randomized controlled trial. (2021).
de Carvalho, T. M., Noels, E., Wakkee, M., Udrea, A. & Nijsten, T. Development of smartphoneapps for skin cancer risk assessment: progress and promise. JMIR Dermatol. 2, e13376.4 (2019).
Google Scholar
NZA. Open DIS Data, (2022).
Zorginstituut, N. Referentiebestand FKGs, (2019).
CPB SES scores, (2017).
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate – a practical and powerful approach to multiple testing. J R Stat Soc B 57, 289–300 (1995).
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