Development of a LED light therapy device with power density control using a Fuzzy logic controller

Development of a near infrared region based non-invasive therapy device for diabetic peripheral neuropathy

Diabetic Peripheral Neuropathy (DPN) is a nerve damage that is treated with painkillers and steroids which have the drawback of interference with other medications and the dangers of side effects. Novelty of the proposed work is to develop a Near Infrared Region (NIR) based non-invasive therapy device called 'DPNrelief-1.0V'developed with a 890 nm wavelength diodes. DPNrelief-1.0V delivers a total dosage of 6.174 J/cm2 with heat absorption by tissue of 61.74 Joules at 30 minutes. The device was tested by carrying out a pilot study with 8 patients where 4 were treatment group and control group. The DPNrelief-1.0V is validated by Nerve Conduction Study (NCS) test. The degenerated nerves pre-therapy showed less amplitude, Conduction Velocity (CV) and latency which was improved post-therapy by 100% in amplitude of nerve signal, 100% in CV and a decrease of 36.2% in latency. Independent t-test was conducted to find the difference between control and treatment, wherein a p value < 0.05 was obtained depicting significant difference between two groups. Furthermore, the performance of the device is validated by one-way test repeated measures Analysis of Variance (ANOVA), wherein a p value of < 0.05 was obtained depicting a difference in nerve condition pre-and post-therapy. The performance of DPNrelief-1.0V has outperformed Anodyne therapy device with lesser dosage, treatment time and portability and in curing the symptoms of DPN. DPNrelief-1.0V finds its potential in the field of medicine for treating DPN.

The role of photobiomodulation in accelerating bone repair

Bone repair is faced with obstacles such as slow repair rates and limited bone regeneration capacity. Delayed healing even nonunion could occur in bone defects, influencing the life quality of patients severely. Photobiomodulation (PBM) utilizes different light sources to derive beneficial therapeutic effects with the advantage of being non-invasive and painless, providing a promising strategy for accelerating bone repair. In this review, we summarize the parameters, mechanisms, and effects of PBM regulating bone repair, and further conclude the current clinical application of PBM devices in bone repair. The wavelength of 635-980 nm, the output power of 40-100 mW, and the energy density of less than 100 J/cm2 are the most commonly used parameters. New technologies, including needle systems and biocompatible and implantable optical fibers, offer references to realize an efficient and safe strategy for bone repair. Further research is required to establish the reliability of outcomes from in vivo and in vitro studies and to standardize clinical trial protocols.

Smart Low Level Laser Therapy System for Automatic Facial Dermatological Disorder Diagnosis

Computer-aided diagnosis using dermoscopy images is a promising technique for improving the efficiency of facial skin disorder diagnosis and treatment. Hence, in this study, we propose a low-level laser therapy (LLLT) system with a deep neural network and medical internet of things (MIoT) assistance. The main contributions of this study are to (1) provide a comprehensive hardware and software design for an automatic phototherapy system, (2) propose a modified-U2Net deep learning model for facial dermatological disorder segmentation, and (3) develop a synthetic data generation process for the proposed models to address the issue of the limited and imbalanced dataset. Finally, a MIoT-assisted LLLT platform for remote healthcare monitoring and management is proposed. The trained U2-Net model achieved a better performance on untrained dataset than other recent models, with an average Accuracy of 97.5%, Jaccard index of 74.7%, and Dice coefficient of 80.6%. The experimental results demonstrated that our proposed LLLT system can accurately segment facial skin diseases and automatically apply for phototherapy. The integration of artificial intelligence and MIoT-based healthcare platforms is a significant step toward the development of medical assistant tools in the near future.

Enhanced precision of real-time control photothermal therapy using cost-effective infrared sensor array and artificial neural network

Photothermal therapy (PTT) requires tight thermal dose control to achieve tumor ablation with minimal thermal injury on surrounding healthy tissues. In this study, we proposed a real-time closed-loop system for monitoring and controlling the temperature of PTT using a non-contact infrared thermal sensor array and an artificial neural network (ANN) to induce a predetermined area of thermal damage on the tissue. A cost-effective infrared thermal sensor array was used to monitor the temperature development for feedback control during the treatment. The measured and predicted temperatures were used as inputs of fuzzy control logic controllers that were implemented on an embedded platform (Jetson Nano) for real-time thermal control. Three treatment groups (continuous wave = CW, conventional fuzzy logic = C-Fuzzy, and ANN-based predictive fuzzy logic = P-Fuzzy) were examined and compared to investigate the laser heating performance and collect temperature data for ANN model training. The ex vivo experiments validated the efficiency of fuzzy control with temperature method on maintaining the constant interstitial tissue temperature (80 ± 1.4 ^°C) at a targeted surface of the tissue. The linear relationship between coagulation areas and the treatment time was indicated in this study, with the averaged coagulation rate of 0.0196 cm²/s. A thermal damage area of 1.32 cm² (diameter ∼1.3 cm) was observed under P-Fuzzy condition for 200 s, which covered the predetermined thermal damage area (diameter ∼1 cm). The integration of real-time feedback temperature control with predictive ANN could be a feasible approach to precisely induce the preset extent of thermal coagulation for treating papillary thyroid microcarcinoma.

A smart LED therapy device with an automatic facial acne vulgaris diagnosis based on deep learning and internet of things application

In low-level laser therapy, providing an optimal dosage and proposing a proper diagnosis before dermatological treatment are essential to reduce the side effects and potential dangers. In this article, a smart LED therapy system for automatic facial acne vulgaris diagnosis based on deep learning and Internet of Things application is proposed. The main goals of this study were to (1) develop an LED therapy device with different power densities and LED grid control; (2) propose a deep learning model based on modified ResNet50 and YOLOv2 for an automatic acne diagnosis; and (3) develop a smartphone application for facial photography image capture and LED therapy parameter configuration. Furthermore, a healthcare Internet of Things (H-IoT) platform for the connectivity between smartphone apps, the cloud server, and the LED therapy device is proposed to improve the efficiency of the treatment process. Experiments were conducted on test data sets divided by a cross-validation method to verify the feasibility of the proposed LED therapy system with automatic facial acne detection. The obtained results evidenced the practical application of the proposed LED therapy system for automatic acne diagnosis and H-IoT-based solutions.