Artificial intelligence and machine learning algorithms for early detection of skin cancer in community and primary care settings: a systematic review
Tài liệu tham khảo
Garbe, 2021, Epidemiology of cutaneous melanoma and keratinocyte cancer in white populations 1943–2036, Eur J Cancer, 152, 18, 10.1016/j.ejca.2021.04.029
Karimkhani, 2015, It's time for “keratinocyte carcinoma” to replace the term “nonmelanoma skin cancer”, J Am Acad Dermatol, 72, 186, 10.1016/j.jaad.2014.09.036
Karia, 2016, Epidemiology and outcomes of cutaneous squamous cell carcinoma, 3
Hiom, 2015, Diagnosing cancer earlier: reviewing the evidence for improving cancer survival, Br J Cancer, 112, S1, 10.1038/bjc.2015.23
Esteva, 2017, Dermatologist-level classification of skin cancer with deep neural networks, Nature, 542, 115, 10.1038/nature21056
Haenssle, 2018, 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, 10.1093/annonc/mdy166
Brinker, 2019, Deep neural networks are superior to dermatologists in melanoma image classification, Eur J Cancer, 119, 11, 10.1016/j.ejca.2019.05.023
Han, 2020, Augmented intelligence dermatology: deep neural networks empower medical professionals in diagnosing skin cancer and predicting treatment options for 134 skin disorders, J Invest Dermatol, 140, 1753, 10.1016/j.jid.2020.01.019
Maron, 2020, Artificial intelligence and its effect on dermatologists' accuracy in dermoscopic melanoma image classification: web-based survey study, J Med Internet Res, 22, 10.2196/18091
Tschandl, 2020, Human-computer collaboration for skin cancer recognition, Nat Med, 26, 1229, 10.1038/s41591-020-0942-0
Liu, 2019, A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis, Lancet Digit Health, 1, e271, 10.1016/S2589-7500(19)30123-2
Challen, 2019, Artificial intelligence, bias and clinical safety, BMJ Qual Saf, 28, 231, 10.1136/bmjqs-2018-008370
Walter, 2019, Evaluating diagnostic strategies for early detection of cancer: the CanTest framework, BMC Cancer, 19, 586, 10.1186/s12885-019-5746-6
Jones, 2021, Artificial intelligence techniques that may be applied to primary care data to facilitate earlier diagnosis of cancer: systematic review, J Med Internet Res, 23, 10.2196/23483
Moher, 2015, Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement, Syst Rev, 4, 1, 10.1186/2046-4053-4-1
Jones
Freeman, 2020, Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies, BMJ, 368, m127, 10.1136/bmj.m127
McCarthy, 2006, A proposal for the Dartmouth Summer Research Project on Artificial Intelligence, August 31, 1955, AI Magazine, 27, 12
Muehlhauser, 2016
Whiting, 2011, QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies, Ann Intern Med, 155, 529, 10.7326/0003-4819-155-8-201110180-00009
Popay
Roffman, 2018, Predicting non-melanoma skin cancer via a multi-parameterized artificial neural network, Sci Rep, 8, 10.1038/s41598-018-19907-9
Udrea, 2020, Accuracy of a smartphone application for triage of skin lesions based on machine learning algorithms, J Eur Acad Dermatol Venereol, 34, 648, 10.1111/jdv.15935
Tschandl, 2019, 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, 10.1016/S1470-2045(19)30333-X
Lee, 2020, Augmented decision-making for acral lentiginous melanoma detection using deep convolutional neural networks, J Eur Acad Dermatol Venereol, 34, 1842, 10.1111/jdv.16185
Lucius, 2020, Deep neural frameworks improve the accuracy of general practitioners in the classification of pigmented skin lesions, Diagnostics (Basel), 10, 969, 10.3390/diagnostics10110969
Sevli, 2021, A deep convolutional neural network-based pigmented skin lesion classification application and experts evaluation, Neural Comput Appl, 33, 12039, 10.1007/s00521-021-05929-4
Phillips, 2019, Assessment of accuracy of an artificial intelligence algorithm to detect melanoma in images of skin lesions, JAMA Netw Open, 2, 10.1001/jamanetworkopen.2019.13436
Liu, 2020, A deep learning system for differential diagnosis of skin diseases, Nat Med, 26, 900, 10.1038/s41591-020-0842-3
Veronese, 2021, The role in teledermoscopy of an inexpensive and easy-to-use smartphone device for the classification of three types of skin lesions using convolutional neural networks, Diagnostics (Basel), 11, 451, 10.3390/diagnostics11030451
Aggarwal, 2021, Artificial intelligence image recognition of melanoma and basal cell carcinoma in racially diverse populations, J Dermatolog Treat
Haenssle, 2020, Man against machine reloaded: performance of a market-approved convolutional neural network in classifying a broad spectrum of skin lesions in comparison with 96 dermatologists working under less artificial conditions, Ann Oncol, 31, 137, 10.1016/j.annonc.2019.10.013
MacLellan, 2021, The use of noninvasive imaging techniques in the diagnosis of melanoma: a prospective diagnostic accuracy study, J Am Acad Dermatol, 85, 353, 10.1016/j.jaad.2020.04.019
Sies, 2020, Past and present of computer-assisted dermoscopic diagnosis: performance of a conventional image analyser versus a convolutional neural network in a prospective data set of 1,981 skin lesions, Eur J Cancer, 135, 39, 10.1016/j.ejca.2020.04.043
Winkler, 2020, Melanoma recognition by a deep learning convolutional neural network–performance in different melanoma subtypes and localisations, Eur J Cancer, 127, 21, 10.1016/j.ejca.2019.11.020
Phillips, 2019, Detection of malignant melanoma using artificial intelligence: an observational study of diagnostic accuracy, Dermatol Pract Concept, 10
Muñoz-López, 2021, Performance of a deep neural network in teledermatology: a single-centre prospective diagnostic study, J Eur Acad Dermatol Venereol, 35, 546, 10.1111/jdv.16979
Jain, 2021, Development and assessment of an artificial intelligence-based tool for skin condition diagnosis by primary care physicians and nurse practitioners in teledermatology practices, JAMA Netw Open, 4, 10.1001/jamanetworkopen.2021.7249
Usher-Smith, 2016, The spectrum effect in tests for risk prediction, screening, and diagnosis, BMJ, 353
Wen, 2022, Characteristics of publicly available skin cancer image datasets: a systematic review, Lancet Digit Health, 4, e64, 10.1016/S2589-7500(21)00252-1
Brinker, 2018, Skin cancer classification using convolutional neural networks: systematic review, J Med Internet Res, 20, 10.2196/11936
Dick, 2019, Accuracy of computer-aided diagnosis of melanoma: a meta-analysis, JAMA Dermatol, 155, 1291, 10.1001/jamadermatol.2019.1375
Obermeyer, 2021, Artificial intelligence, bias, and patients' perspectives, Lancet, 397, 10.1016/S0140-6736(21)01152-1
Ibrahim, 2021, Health data poverty: an assailable barrier to equitable digital health care, Lancet Digit Health, 3, e260, 10.1016/S2589-7500(20)30317-4
Polesie, 2020, Attitudes towards artificial intelligence within dermatology: an international online survey, Br J Dermatol, 183, 159, 10.1111/bjd.18875
Jutzi, 2020, Artificial intelligence in skin cancer diagnostics: the patients' perspective, Front Med (Lausanne), 7, 233, 10.3389/fmed.2020.00233
Welch, 2021, The rapid rise in cutaneous melanoma diagnoses, N Engl J Med, 384, 72, 10.1056/NEJMsb2019760
Sounderajah, 2020, Developing specific reporting guidelines for diagnostic accuracy studies assessing AI interventions: the STARD-AI Steering Group, Nat Med, 26, 807, 10.1038/s41591-020-0941-1
Collins, 2019, Reporting of artificial intelligence prediction models, Lancet, 393, 1577, 10.1016/S0140-6736(19)30037-6
Liu, 2019, Reporting guidelines for clinical trials evaluating artificial intelligence interventions are needed, Nat Med, 25, 1467, 10.1038/s41591-019-0603-3
Liu, 2020, Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension, Nat Med, 26, 1364, 10.1038/s41591-020-1034-x
Rivera, 2020, Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI Extension, BMJ, 370