An artificial intelligence based app for skin cancer detection evaluated in a population based setting

0
  • 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).

    Article 
    PubMed 

    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).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Chen, S. T., Geller, A. C. & Tsao, H. Update on the epidemiology of melanoma. Curr. Dermatol. Rep. 2, 24–34 (2013).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Janda, M. & Soyer, H. P. Can clinical decision making be enhanced by artificial intelligence? Br. J. Dermatol. 180, 247–248 (2019).

    Article 
    CAS 
    PubMed 

    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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Esteva, A. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115–118 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    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).

    Article 
    CAS 
    PubMed 

    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).

    Article 
    PubMed 
    PubMed Central 

    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).

    Article 
    CAS 
    PubMed 

    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).

    Article 
    CAS 
    PubMed 

    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).

    Article 
    PubMed 

    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).

    Article 
    PubMed 
    PubMed Central 

    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).

    PubMed 

    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).

    Article 
    PubMed 
    PubMed Central 

    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).

    Article 
    PubMed 

    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).

    Article 
    PubMed 

    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).

    Article 
    PubMed 

    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).

    Article 
    CAS 
    PubMed 

    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).

    Article 
    PubMed 
    PubMed Central 

    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).

    Article 
    PubMed 

    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).

    Article 
    PubMed 

    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).

    Article 
    PubMed 

    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).

    Article 
    PubMed 

    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).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    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).

    Article 
    PubMed 

    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).

    Article 
    PubMed 

    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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Ran, T. et al. Cost-effectiveness of colorectal cancer screening strategies-a systematic review. Clin. Gastroenterol. Hepatol. 17, 1969–1981 (2019).

    Article 
    PubMed 

    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).

    Article 
    PubMed 

    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).

    Article 
    PubMed 

    Google Scholar 

  • Pearce, N. Analysis of matched case-control studies. BMJ 352, i969 (2016).

    Article 
    PubMed 
    PubMed Central 

    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).

    Article 
    PubMed 

    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).

    Article 

    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).

    Google Scholar 

  • link

    Leave a Reply

    Your email address will not be published. Required fields are marked *