Melanoma diagnosis at the time of COVID‐19

Abstract

learning, there are no correct output values. Instead, algorithms are able to function freely in order to learn more about the data and present interesting findings. It is popular in the applications of clustering or the act of uncovering groups within data and association, or act of predicting rules that describe the data. In a study by Kharazmi et al., 4 basal cell carcinoma (BCC) was detected by unsupervised learning framework to learn comprehensive BCC vascular features on dermoscopy directly from the data instead of manually designing multiple sets of patternspecific features for every vascular type like in conventional supervised learning. The most important advantage of supervised learning models over unsupervised is the precise information regarding the data, but classifying big data can be a real challenge in supervised learning, and this system has trouble dealing with new information too. There should be frequent use of the unsupervised method in ML as the supervised method is incapable of satisfactory performance in situations where the statistics vary substantially. The clustering in unsupervised learning has an advantage in cases where images look substantially different from one another. There is an advantage of the omitting training phase, which is a tedious step in the procedure. In unsupervised learning, the greatest source of error is likely to be the shape of the skin lesion images. Pixels of skin have equal representation whether they are of the actual skin lesion or the surrounding healthy skin, and this may cause significant confusion. A segmentation algorithm can somehow ensure that the unsupervised algorithm focuses on the damaged skin. 5 Reinforcement learning, another category of ML, is a hybrid of both supervised and unsupervised learning which learns by trial and error and input from the environment, but it is yet to be explored in dermatology. 6 There is a concept of semi-supervised learning, which uses both labeled and unlabeled data to overcome the problems of supervised learning when there is not enough labeled data. This approach is more practical as it can operate even when there is less availability of labeled data by unlabeled data. Labeling of data in dermatology in terms of images is a tough task. This approach may prove to be a better and safer ML approach in dermatology; therefore, it warrants more clinical trials and studies in the future.

COVID‐19‐related consequences on melanoma diagnoses from a local Italian registry in Genoa, Italy

“…The total number of melanoma diagnoses was 6/163 in 2020 versus 24/246 in 2019. A similar situation has been reported by Longo et al 4 . with a reduction of new melanoma diagnoses from January 1, 2019, to May 9, 2019, (141 in Rome and 115 in Reggio Emilia) and the same timeframe in 2020 (62 in Rome and 28 in Reggio Emilia) 4 .…”

Effects of COVID-19 Lockdown on Melanoma Diagnosis in Switzerland: Increased Tumor Thickness in Elderly Females and Shift towards Stage IV Melanoma during Lockdown

“…We assume that due to lockdown measures and the lack of skin cancer screenings, certain patients have experienced a delay, even if not quantified statistically, which has led to the rapid increase in new melanoma cases after the lift of major restrictions in our study. Various studies pointed out that the delay in the diagnosis of melanomas due to COVID-19-related restrictions was associated with a reduction in the number of skin cancer diagnoses of up to 60% [6,27,28]. Among all cancers, cutaneous malignancies were the ones with the highest number of missed diagnoses with a diagnostic decrease of 56.7% in 2020 vs. 2019 [27].…”

Impact of the COVID-19 Pandemic on Melanoma Diagnosis: Increased Breslow Thickness in Primary Melanomas—A Single Center Experience

“…Depending on its capacity, every nation has since then implemented different measures dedicated to stopping the spread of the disease as well as managing an exponentially growing number of newly diagnosed patients. Many studies showed that these restrictions have led to a drastic drop in the number of newly diagnosed skin tumors, some even by 60% [ 30 , 38 , 44 ]. Out of all patients with malignancies, skin tumor patients have been the ones with the most missed appointments, with a 56.7% decrease in diagnoses in 2020 when compared to the previous year [ 45 ].…”