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

Advances in Photonic Materials and Integrated Devices for Smart and Digital Healthcare: Bridging the Gap Between Materials and Systems

Recent advances in developing photonic technologies using various materials offer enhanced biosensing, therapeutic intervention, and non-invasive imaging in healthcare. Here, this article summarizes significant technological advancements in materials, photonic devices, and bio-interfaced systems, which demonstrate successful applications for impacting human healthcare via improved therapies, advanced diagnostics, and on-skin health monitoring. The details of required materials, necessary properties, and device configurations are described for next-generation healthcare systems, followed by an explanation of the working principles of light-based therapeutics and diagnostics. Next, this paper shares the recent examples of integrated photonic systems focusing on translation and immediate applications for clinical studies. In addition, the limitations of existing materials and devices and future directions for smart photonic systems are discussed. Collectively, this review article summarizes the recent focus and trends of technological advancements in developing new nanomaterials, light delivery methods, system designs, mechanical structures, material functionalization, and integrated photonic systems to advance human healthcare and digital healthcare.

Deep learning enabled in vitro predicting biological tissue thickness using force measurement device

Accurate perception of biological tissues (BT) thickness is essential for preliminary evaluation of medical diagnosis and animal nutrition. However, traditional thickness measuring approaches of BT require complex operation, high-cost, and trigger biological stress response. Herein this study, an novel in vitro BT thickness measuring approach integrated with force test system (FST) and the discrete multiwavelet transform convolutional neural network (DMWA-CNN) prediction model based on deep learning are proposed. Simultaneously, several comprehensive experiments and model comparisons are conducted to demonstrate the superiority of the proposed approach. By establishing a DMWA-CNN demonstrates higher estimation accuracy than other traditional algorithm, achieving 100 % accuracy for artificial BT. Moreover, the experimental results indicate that proposed approach is robust to elastic modulus variation (E), external load variation (F), and small thickness differences (Ts). In addition, four kinds of the pork' thickness are experimentally measured, and the accuracy value is not less than 98.2 %. The thickness of BT determined using the FST and DMWA-CNN algorithm demonstrate potential application in the biomechanical parameter prediction.

Digital twins in dermatology, current status, and the road ahead

Digital twins, innovative virtual models synthesizing real-time biological, environmental, and lifestyle data, herald a new era in personalized medicine, particularly dermatology. These models, integrating medical-purpose Internet of Things (IoT) devices, deep and digital phenotyping, and advanced artificial intelligence (AI), offer unprecedented precision in simulating real-world physical conditions and health outcomes. Originating in aerospace and manufacturing for system behavior prediction, their application in healthcare signifies a paradigm shift towards patient-specific care pathways. In dermatology, digital twins promise enhanced diagnostic accuracy, optimized treatment plans, and improved patient monitoring by accommodating the unique complexities of skin conditions. However, a comprehensive review across PubMed, Embase, Web of Science, Cochrane, and Scopus until February 5th, 2024, underscores a significant research gap; no direct studies on digital twins' application in dermatology is identified. This gap signals challenges, including the intricate nature of skin diseases, ethical and privacy concerns, and the necessity for specialized algorithms. Overcoming these barriers through interdisciplinary efforts and focused research is essential for realizing digital twins' potential in dermatology. This study advocates for a proactive exploration of digital twins, emphasizing the need for a tailored approach to dermatological care that is as personalized as the patients themselves.

A Topical Review on Enabling Technologies for the Internet of Medical Things: Sensors, Devices, Platforms, and Applications

The Internet of Things (IoT) is still a relatively new field of research, and its potential to be used in the healthcare and medical sectors is enormous. In the last five years, IoT has been a go-to option for various applications such as using sensors for different features, machine-to-machine communication, etc., but precisely in the medical sector, it is still lagging far behind compared to other sectors. Hence, this study emphasises IoT applications in medical fields, Medical IoT sensors and devices, IoT platforms for data visualisation, and artificial intelligence in medical applications. A systematic review considering PRISMA guidelines on research articles as well as the websites on IoMT sensors and devices has been carried out. After the year 2001, an integrated outcome of 986 articles was initially selected, and by applying the inclusion-exclusion criterion, a total of 597 articles were identified. 23 new studies have been finally found, including records from websites and citations. This review then analyses different sensor monitoring circuits in detail, considering an Intensive Care Unit (ICU) scenario, device applications, and the data management system, including IoT platforms for the patients. Lastly, detailed discussion and challenges have been outlined, and possible prospects have been presented.

Flexible Quantum Dot Light-Emitting Device for Emerging Multifunctional and Smart Applications

Quantum dot light-emitting diodes (QLEDs), owing to their exceptional performances in device efficiency, color purity/tunability in the visible region and solution-processing ability on various substrates, become a potential candidate for flexible and ultrathin electroluminescent (EL) lighting and display. Moreover, beyond the lighting and display, flexible QLEDs are enabled with endless possibilities in the era of the internet of things and artificial intelligence by acting as input/output ports in wearable integrated systems. Challenges remain in the development of flexible QLEDs with the goals for high performance, excellent flexibility/even stretchability, and emerging applications. In this paper, the recent developments of QLEDs including quantum dot materials, working mechanism, flexible/stretchable strategies and patterning strategies, and highlight its emerging multifunctional integrations and smart applications covering wearable optical medical devices, pressure-sensing EL devices, and neural smart EL devices, are reviewed. The remaining challenges are also summarized and an outlook on the future development of flexible QLEDs made. The review is expected to offer a systematic understanding and valuable inspiration for flexible QLEDs to simultaneously satisfy optoelectronic and flexible properties for emerging applications.

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.

AcneTyper: An automatic diagnosis method of dermoscopic acne image via self-ensemble and stacking

BACKGROUND

Acne is a skin lesion type widely existing in adolescents, and poses computational challenges for automatic diagnosis. Computer vision algorithms are utilized to detect and determine different subtypes of acne. Most of the existing acne detection algorithms are based on the facial natural images, which carry noisy factors like illuminations.

OBJECTIVE

In order to tackle this issue, this study collected a dataset ACNEDer of dermoscopic acne images with annotations. Deep learning methods have demonstrated powerful capabilities in automatic acne diagnosis, and they usually release the training epoch with the best performance as the delivered model.

METHODS

This study proposes a novel self-ensemble and stacking-based framework AcneTyper for diagnosing the acne subtypes. Instead of delivering the best epoch, AcneTyper consolidates the prediction results of all training epochs as the latent features and stacks the best subset of these latent features for distinguishing different acne subtypes.

RESULTS

The proposed AcneTyper framework achieves a promising detection performance of acne subtypes and even outperforms a clinical dermatologist with two-year experiences by 6.8% in accuracy.

CONCLUSION

The method we proposed is used to determine different subtypes of acne and outperforms inexperienced dermatologists and contributes to reducing the probability of misdiagnosis.

A Flexible, Wearable, and Wireless Biosensor Patch with Internet of Medical Things Applications

Monitoring the vital signs and physiological responses of the human body in daily activities is particularly useful for the early diagnosis and prevention of cardiovascular diseases. Here, we proposed a wireless and flexible biosensor patch for continuous and longitudinal monitoring of different physiological signals, including body temperature, blood pressure (BP), and electrocardiography. Moreover, these modalities for tracking body movement and GPS locations for emergency rescue have been included in biosensor devices. We optimized the flexible patch design with high mechanical stretchability and compatibility that can provide reliable and long-term attachment to the curved skin surface. Regarding smart healthcare applications, this research presents an Internet of Things-connected healthcare platform consisting of a smartphone application, website service, database server, and mobile gateway. The IoT platform has the potential to reduce the demand for medical resources and enhance the quality of healthcare services. To further address the advances in non-invasive continuous BP monitoring, an optimized deep learning architecture with one-channel electrocardiogram signals is introduced. The performance of the BP estimation model was verified using an independent dataset; this experimental result satisfied the Association for the Advancement of Medical Instrumentation, and the British Hypertension Society standards for BP monitoring devices. The experimental results demonstrated the practical application of the wireless and flexible biosensor patch for continuous physiological signal monitoring with Internet of Medical Things-connected healthcare applications.

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.