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Go to Editorial ManagerMany difficulties were recorded during laser-assisted tattoo removal. But most of them remain unknown. The recent literatures on laser tattoo removal focuses more on removal methods and systems than on side effects, such as temperature increase over tissue and ideal treatment parameters. This study aims to assess the surface temperature in compliance with eyebrow tattoo removal. The study was carried out for 55 patients aged between 22 and 43 years. The treatment was performed using a Nd:YAG laser (1064nm, Phi laser system) with an energy of 1000 mJ, a frequency of 3Hz, and a spot size of 8mm. The surface temperature of the skin during tattoo removal process was measured with a FLIR thermal camera. The results were analyzed by testing the normal state of distribution. The Shapiro-Wilk and Kolmogorov-Smirnov tests were used. All patients finished the full treatment of three laser sessions to achieve the goal of total removal. After temperature comparison, the results showed a significant influence of skin nature and patients' age on temperature distribution on skin, as for older patients, the energy absorption increased. Additionally, patients with darker skin tones exhibited greater absorption. The benefit of deepening understanding appeared in the Temperature distribution in the tissues of the affected area and the surrounding area during laser irradiation, as it provides a guiding and reference function for the effect of photothermal therapy.
In order to avoid losing sense of sight in a large portion of the working population, Diabetic Retinopathy (DR) identification during broad examination for diabetes is crucial. To prevent blindness in the future, early illness detection and measurement of disease development are essential. DR is diagnosed through medical image analysis. After the success of Deep Learning (DL) in other applications in the real world, it is considered a vital tool for upcoming health sector applications, providing solutions with accurate results for medical image analysis. This review provides a comprehensive survey of the state-of-the-art DL models for DR detection and grading using retinal fundus photography. This review thoroughly examined and summarized 81 relevant publications that were published through IEEE Xplore, Web of Science, PubMed, and Scopus between 2018 and 2023 based on the available database with binary or multiclass CNN classification models as well as the main preprocessing techniques. According to the findings of this review, transfer learning has proven to be an excellent technique for addressing the problems of limited resources for data for DR analysis. CNN models having tens or hundreds of layers are the most frequently utilized frameworks for DR classification. The most extensively utilized datasets for DR categorization are Aptos 2019 and EyePACS. Although DL has attained or surpassed human-level DR classification accuracy, there is still more work to be done in real-world clinical procedures.