×
The submission system is temporarily under maintenance. Please send your manuscripts to
Go to Editorial ManagerNon-dispersive near-infrared technique is widely used nowadays for the detection of gases, especially in harsh environments. In this study, an optical gas cell was designed for oxygenator exhaust capnometry. A computer-based simulation was used for the analysis of air flows for model selection. ANSYS Discovery 2020 R2 was used for model simulation. The gas flow cells were tested using a custom-made gas rig to measure the fraction absorbance of carbon dioxide gas at the detector. Two gases were used, nitrogen gas as a reference gas (0%) and 9% carbon dioxide. Three gas cells with the following optical path lengths were tested: 31mm, 36mm, and 40mm. The results showed that all gas flow cells produced laminar flow and small pressure drop across the inlet and outlet of the cell (11~12 Pa). Further, the minimum velocity is obtained in the 40mm gas flow sensor and it is located at the gas outlet path away from the effective optical gas path. The simulation and experimental results indicate that the gas flow cell of 40mm optical path length is more suitable for the intended application as it offers a maximum effective absorption path compared to the stagnation areas, and as a result, it provides the maximum fraction absorbance.
Many 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.
This research focuses on enhancing the diagnostic power of the slit lamp, a fundamental ophthalmic instrument, by replacing its traditional halogen light source with a cutting-edge white laser. The objective of this modification is to significantly improve the brightness, intensity, and color accuracy, which are crucial for distinguishing fine ocular details during eye examinations. White laser technology offers a more stable, energy-efficient light source with reduced maintenance needs, making it a valuable upgrade over conventional systems. As part of this redesign, the optical system will be optimized with new filters, lenses, and heat management techniques to accommodate the white laser. Additionally, integrating a high-resolution digital camera with the enhanced illumination system is expected to provide sharper, more accurate imaging for better diagnosis. The anticipated outcome is a transformative improvement in ocular diagnostics, leading to earlier and more precise detection of eye conditions. This advancement holds promise for both patients, through better care, and ophthalmologists, through increased diagnostic efficiency. Challenges in implementation and potential solutions are also considered.
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.