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Teledermatology has been proving to be of great help for delivering healthcare, especially now, during the SARS-CoV-2 pandemic. It is crucial to assess how accurate this method can be for evaluating different dermatoses. Such knowledge can contribute to the dermatologists' decision of whether to adhere to teledermatology or not. Our objective was to determine the accuracy of teledermatology in the 10 most frequent skin neoplasms in our population, comparing telediagnosis to histopathological report and in-person dermatologists' diagnosis. A retrospective cohort study was conducted in São Paulo, Brazil, where a store-and-forward teledermatology project was implemented under primary-care attention to triage surgical, more complex, or severe dermatoses. A total of 30,976 patients presenting 55,012 lesions took part in the project. Thirteen teledermatologists who participated in the project had three options to refer the patients: send them directly to biopsy, to the in-person dermatologist, or back to the general physician with the most probable diagnosis and management. In the groups referred to the in-person dermatologist and biopsy, we looked for the 10 most frequent International Statistical Classification of Diseases and Related Health Problems-10 (ICD-10) of skin neoplasms, which resulted in 289 histopathologic reports and 803 in-person dermatologists' diagnosis. We were able to compare the ICD-10 codes filled by teledermatologists, in-person dermatologists, and from histopathological reports. The proportion of complete, partial, and no agreement rates between the in-person dermatologist's, histopathologic report, and the teledermatologist's diagnosis was assessed. We also calculated Cohen's kappa, for complete and complete plus partial agreement. The mean complete agreement rate comparing telediagnosis to histopathological report was 54% (157/289; kappa = 0.087), being the highest for malign lesions; to in-person dermatologists was 61% (487/803; kappa = 0.213), highest for benign lesions. When accuracy of telediagnosis for either malign or benign lesions was evaluated, the agreement rate with histopathology was 70% (kappa = 0.529) and with in-person dermatologist, 81% (kappa = 0.582). This study supports that teledermatology for skin neoplasms has moderate accuracy. This result reassures that it can be a proper option for patient care, especially when the goal is to differentiate benign from malign lesions.

Skin cancer is currently the most common type of cancer among Caucasians. The increase in life expectancy, along with new diagnostic tools and treatments for skin cancer, has resulted in unprecedented changes in patient care and has generated a great burden on healthcare systems. Early detection of skin tumors is expected to reduce this burden. Artificial intelligence (AI) algorithms that support skin cancer diagnoses have been shown to perform at least as well as dermatologists’ diagnoses. Recognizing the need for clinically and economically efficient means of diagnosing skin cancers at early stages in the primary care attention, we developed an efficient computer-aided diagnosis (CAD) system to be used by primary care physicians (PCP). Additionally, we developed a smartphone application with a protocol for data acquisition (i.e., photographs, demographic data and short clinical histories) and AI algorithms for clinical and dermoscopic image classification. For each lesion analyzed, a report is generated, showing the image of the suspected lesion and its respective Heat Map; the predicted probability of the suspected lesion being melanoma or malignant; the probable diagnosis based on that probability; and a suggestion on how the lesion should be managed. The accuracy of the dermoscopy model for melanoma was 89.3%, and for the clinical model, 84.7% with 0.91 and 0.89 sensitivity and 0.89 and 0.83 specificity, respectively. Both models achieved an area under the curve (AUC) above 0.9. Our CAD system can screen skin cancers to guide lesion management by PCPs, especially in the contexts where the access to the dermatologist can be difficult or time consuming. Its use can enable risk stratification of lesions and/or patients and dramatically improve timely access to specialist care for those requiring urgent attention.

Teledermatology is assuming a progressively greater role as a healthcare delivery method, especially now, during this pandemic time. It is important to know how accurate this tool is for different skin diseases. Most of the studies have focused on skin neoplasms or general dermatology. Studies based on a large number of inflammatory dermatoses have not yet been performed. Such knowledge can help dermatologists to decide whether endorsing this method or not. Our objective was to determine the accuracy of teledermatology in inflammatory dermatoses in a robust number of cases. A retrospective cohort study was conducted in São Paulo, Brazil, from July 2017-18, where a store-and-forward Teledermatology project was implemented under primary-care attention to triage surgical, more complex, or severe dermatoses. A total of 30,976 patients presenting 55,012 lesions took part in the project. Thirteen participating teledermatologists had three options to refer the patients: directly to biopsy, to the inperson dermatologist or back to the general physician with most probable diagnosis and management. In the group referred to the in-person dermatologist, we looked for the 20 most frequent International Classification of Diseases and Related Health Problems-10th revision (ICD-10) of inflammatory dermatoses, which resulted in 739 patients and 739 lesions. As patients had been triaged by teledermatology previously, we were able to compare ICD-10 codes filled both by teledermatogists and by in-person dermatologists. The proportion of complete, partial, and no agreement rates between the in-person dermatologist's and the teledermatologist's diagnoses was used for accuracy. We also calculated Cohen's kappa, a statistical measure of inter-rater agreement, for complete agreement. The mean complete agreement rate for all twenty dermatoses was 78% (31-100%) and kappa = 0.743; partial agreement 8%; and no agreement 14%, presenting variability according to the disease. Our study showed that teledermatology for inflammatory dermatoses has a high accuracy. This result reassures that it can be a proper option for patient care.

No abstract

Para mensurar a qualidade do biodiesel no Brasil, vários parâmetros são estabelecidos. Alguns deles como viscosidade, índice de iodo, número de cetano, densidade são importantes pois caracterizam funções importantes de como o biodiesel irá reagir no motores. Neste estudo, foram usadas Redes Neurais Artificiais (RNAs) para predizer o índice de viscosidade do biodiesel. Para este fim foram utilizadas 13 compostos de esteres deácidos graxos como entrada para as RNAs, com vários algoritmos de convergência do tipo feedforward tendo como sáıda das redes os índices de viscosidade.