The Usefulness of the Artificial Intelligence Data in Analyzing the Skin in the Era of the Fourth Industrial Revolution

In the World economy forum Global Challenge Insight Report titled “The Future of Jobs-Employment, Skills and Workforce Strategy for the Fourth Industrial Revolution (FIR) in 2016”, a new industrial revolution was predicted to occur in the near future. This is followed by the opinion that it would be mandatory to prepare for the FIR because it will definitely change people’s way of working, consuming and thinking. There is a controversy as to the potential of AI in health care. To date, however, remarkable achievements have been made in the field of medicine, particularly entailing dermatology. Therefore, this study explored the usefulness of the AI data in analyzing the skin in the era of the FIR. The current study finally included a total of 300 subjects, for whom a self-reporting questionnaire survey was performed between June 09 and July 18, 2020. The current study proposed the following hypothesis: The AI data might be useful in analyzing the skin in the era of the FIR. This hypothesis was accepted. In conclusion, the current study suggests that the AI data might be useful in analyzing the skin in the era of the FIR. But this deserves further study.

Effect of artificial dermis combined with jinfu ning on skin healing and bacterial detection rate of finger abdomen

Objective: To explore the effect of artificial dermis combined with rhGM-CSF(Jinfuning) on healing of soft tissue defect of finger ventral skin and the influence of bacterial detection rate. Methods: Totally 110 patients with finger injury admitted to the rehabilitation department of our department from January 2017 to June 2018 were collected and divided into control group and observation group according to the random number table method with 55 cases in each group. The control group received direct artificial derma lrepairing after thorough debridement, while the observation group received recombinant gm-csf gel coating on the wound surface before artificial dermal repairing, Wound healing, wound inflammation, bacterial detection rate, inflammatory factor expression, follow-up and adverse reactions were compared between the two groups. Results: The wound healing rate of the observation group at 7, 14, 21 and 28 days after treatment was significantly higher than that of the control group (t= 11.211, P =0.000).( T = 14.895, P =0.000;T = 25.346, P=0.000;T =8.247, P=0.000). The wound healing time of the observation group was (19.7±2.3) d, and that of the control group was (27.4±3.3) d. The average wound healing time of the observation group was significantly shorter than that of the control group, and the difference was statistically significant (t=14.197, P= 0.000). Observation group wound inflammation at each time point score was significantly lower than the control group, the group rooms, time points, ·point interaction effect between the comparison, the differences were statistically significant (P <0.05), the observation group wound bacteria detection rate of 7.27% (4 cases) : the control bacteria detection rate was 21.81% (12 cases), difference was statistically significant (chi-square = 4.68, P= 0.0305), the observation group of bacteria detection rate was significantly lower than the control group;The bacteria detected in the two groups were mainly e. coli, tetanus bacillus and fungi. There was no significant difference in the indicators between the two groups before treatment, and the values of inflammatory cytokines il-1 and TNF- IOD in the two groups were significantly decreased after treatment, and the observation group was significantly lower than the control group, with statistically significant differences (P < 0.05). No serious adverse reactions occurred in either group during the treatment. Conclusion: the application of artificial dermals combined with jinfuning can promote wound healing of skin and soft tissue defect of finger abdomen, effectively inhibit bacterial infection of wound surface, reduce inflammation and infection,reducing bacterial detection rate.

A Hybrid Artificial Intelligence Model for Skin Cancer Diagnosis

Melanoma or skin cancer is the most dangerous and deadliest disease.As the incidence and mortality rate of skin cancer increases worldwide,an automated skin cancer detection/classification system is required for early detection and prevention of skin cancer.In this study,a Hybrid Artificial Intelligence Model(HAIM)is designed for skin cancer classification.It uses diverse multi-directional representation systems for feature extraction and an efficient Exponentially Weighted and Heaped Multi-Layer Perceptron(EWHMLP)for the classification.Though the wavelet transform is a powerful tool for signal and image processing,it is unable to detect the intermediate dimensional structures of a medical image.Thus the proposed HAIM uses Curvelet(CurT),Contourlet(ConT)and Shearlet(SheT)transforms as feature extraction techniques.Though MLP is very flexible and well suitable for the classification problem,the learning of weights is a challenging task.Also,the optimization process does not converge,and the model may not be stable.To overcome these drawbacks,EWHMLP is developed.Results show that the combined qualities of each transform in a hybrid approach provides an accuracy of 98.33%in a multi-class approach on PH2 database.

Optimal Artificial Intelligence Based Automated Skin Lesion Detection and Classification Model

Skin lesions have become a critical illness worldwide,and the earlier identification of skin lesions using dermoscopic images can raise the survival rate.Classification of the skin lesion from those dermoscopic images will be a tedious task.The accuracy of the classification of skin lesions is improved by the use of deep learning models.Recently,convolutional neural networks(CNN)have been established in this domain,and their techniques are extremely established for feature extraction,leading to enhanced classification.With this motivation,this study focuses on the design of artificial intelligence(AI)based solutions,particularly deep learning(DL)algorithms,to distinguish malignant skin lesions from benign lesions in dermoscopic images.This study presents an automated skin lesion detection and classification technique utilizing optimized stacked sparse autoen-coder(OSSAE)based feature extractor with backpropagation neural network(BPNN),named the OSSAE-BPNN technique.The proposed technique contains a multi-level thresholding based segmentation technique for detecting the affected lesion region.In addition,the OSSAE based feature extractor and BPNN based classifier are employed for skin lesion diagnosis.Moreover,the parameter tuning of the SSAE model is carried out by the use of sea gull optimization(SGO)algo-rithm.To showcase the enhanced outcomes of the OSSAE-BPNN model,a comprehensive experimental analysis is performed on the benchmark dataset.The experimentalfindings demonstrated that the OSSAE-BPNN approach outper-formed other current strategies in terms of several assessment metrics.

Betaine combined with traditional Chinese medicine ointment to treat skin wounds in microbially infected diabetic mice

BACKGROUND Skin wounds are highly common in diabetic patients,and with increasing types of pathogenic bacteria and antibiotic resistance,wounds and infections in diabetic patients are difficult to treat and heal.AIM To explore the effects of betaine ointment(BO)in promoting the healing of skin wounds and reducing the inflammation and apoptosis of skin cells in microbially infected diabetic mice.METHODS By detecting the minimum inhibitory concentrations(MICs)of betaine and plant monomer components such as psoralen,we prepared BO with betaine as the main ingredient,blended it with traditional Chinese medicines such as gromwell root and psoralen,and evaluated its antibacterial effects and safety in vitro and in vivo.The skin infection wound models of ordinary mice and diabetic mice were constructed,and the OTC drugs mupirocin ointment and Zicao ointment were used as controls to evaluate the antibacterial effects in vivo and the anti-inflammatory and anti-apoptotic effects of BO.RESULTS The MICs of betaine against microorganisms such as Staphylococcus aureus(S.aureus),Candida albicans and Cryptococcus neoformans ranged from 4 to 32μg/mL.Gromwell root and psoralea,both of which contain antimicrobial components,mixed to prepare BO with MICs ranging from 16 to 64μg/mL,which is 32-256 times lower than those of Zicao ointment,although the MIC is greater than that of betaine.After 15 days of treatment with BO for USA300-infected ordinary mice,the wound scab removal rates were 83.3%,while those of mupirocin ointment and Zicao ointment were 66.7%and 0%,respectively,and the differences were statistically significant.In diabetic mice,the wound scab removal rate of BO and mupirolacin ointment was 80.0%,but BO reduced wound inflammation and the apoptosis of skin cells and facilitated wound healing.CONCLUSION The ointment prepared by mixing betaine and traditional Chinese medicine can effectively inhibit common skin microorganisms and has a strong effect on the skin wounds of sensitive or drug-resistant S.aureus-infected ordinary mice and diabetic mice.

Acellular fish skin grafts in diabetic foot ulcer care:Advances and clinical insights

Diabetic foot ulcers(DFUs)represents a significant public health issue,with a rising global prevalence and severe potential complications including amputation.Traditional treatments often fall short due to various limitations such as high recurrence rates and extensive resource utilization.This editorial explores the innovative use of acellular fish skin grafts as a transformative approach in DFU management.Recent studies and a detailed case report highlight the efficacy of acellular fish skin grafts in accelerating wound closure,reducing dressing changes,and enhancing patient outcomes with a lower socio-economic burden.Despite their promise,challenges such as limited availability,patient acceptance,and the need for further research persist.Addressing these through more extensive randomized controlled trials and fostering a multidisciplinary treatment approach may optimize DFU care and reduce the global health burden associated with these complex wounds.

A Comprehensive Systematic Review: Advancements in Skin Cancer Classification and Segmentation Using the ISIC Dataset

The International Skin Imaging Collaboration(ISIC)datasets are pivotal resources for researchers in machine learning for medical image analysis,especially in skin cancer detection.These datasets contain tens of thousands of dermoscopic photographs,each accompanied by gold-standard lesion diagnosis metadata.Annual challenges associated with ISIC datasets have spurred significant advancements,with research papers reporting metrics surpassing those of human experts.Skin cancers are categorized into melanoma and non-melanoma types,with melanoma posing a greater threat due to its rapid potential for metastasis if left untreated.This paper aims to address challenges in skin cancer detection via visual inspection and manual examination of skin lesion images,processes historically known for their laboriousness.Despite notable advancements in machine learning and deep learning models,persistent challenges remain,largely due to the intricate nature of skin lesion images.We review research on convolutional neural networks(CNNs)in skin cancer classification and segmentation,identifying issues like data duplication and augmentation problems.We explore the efficacy of Vision Transformers(ViTs)in overcoming these challenges within ISIC dataset processing.ViTs leverage their capabilities to capture both global and local relationships within images,reducing data duplication and enhancing model generalization.Additionally,ViTs alleviate augmentation issues by effectively leveraging original data.Through a thorough examination of ViT-based methodologies,we illustrate their pivotal role in enhancing ISIC image classification and segmentation.This study offers valuable insights for researchers and practitioners looking to utilize ViTs for improved analysis of dermatological images.Furthermore,this paper emphasizes the crucial role of mathematical and computational modeling processes in advancing skin cancer detection methodologies,highlighting their significance in improving algorithmic performance and interpretability.

Recent Advances in Artificial Sensory Neurons:Biological Fundamentals,Devices,Applications,and Challenges

Spike-based neural networks,which use spikes or action potentialsto represent information,have gained a lot of attention because of their high energyefficiency and low power consumption.To fully leverage its advantages,convertingthe external analog signals to spikes is an essential prerequisite.Conventionalapproaches including analog-to-digital converters or ring oscillators,and sensorssuffer from high power and area costs.Recent efforts are devoted to constructingartificial sensory neurons based on emerging devices inspired by the biologicalsensory system.They can simultaneously perform sensing and spike conversion,overcoming the deficiencies of traditional sensory systems.This review summarizesand benchmarks the recent progress of artificial sensory neurons.It starts with thepresentation of various mechanisms of biological signal transduction,followed bythe systematic introduction of the emerging devices employed for artificial sensoryneurons.Furthermore,the implementations with different perceptual capabilitiesare briefly outlined and the key metrics and potential applications are also provided.Finally,we highlight the challenges and perspectives for the future development of artificial sensory neurons.

Single-cell pan-omics, environmental neurology, and artificial intelligence:the time for holistic brain health research

The brain,with its trillions of neural connections,different cellular types,and molecular complexities,presents a formidable challenge for researchers aiming to comprehend the multifaceted nature of neural health.As traditional methods have provided valuable insights,emerging technologies offer unprecedented opportunities to delve deeper into the underpinnings of brain function.In the everevolving landscape of neuroscience,the quest to unravel the mysteries of the human brain is bound to take a leap forward thanks to new technological improvements and bold interpretative frameworks.

Revolutionizing diabetic retinopathy screening and management:The role of artificial intelligence and machine learning

Diabetic retinopathy(DR)remains a leading cause of vision impairment and blindness among individuals with diabetes,necessitating innovative approaches to screening and management.This editorial explores the transformative potential of artificial intelligence(AI)and machine learning(ML)in revolutionizing DR care.AI and ML technologies have demonstrated remarkable advancements in enhancing the accuracy,efficiency,and accessibility of DR screening,helping to overcome barriers to early detection.These technologies leverage vast datasets to identify patterns and predict disease progression with unprecedented precision,enabling clinicians to make more informed decisions.Furthermore,AI-driven solutions hold promise in personalizing management strategies for DR,incorpo-rating predictive analytics to tailor interventions and optimize treatment path-ways.By automating routine tasks,AI can reduce the burden on healthcare providers,allowing for a more focused allocation of resources towards complex patient care.This review aims to evaluate the current advancements and applic-ations of AI and ML in DR screening,and to discuss the potential of these techno-logies in developing personalized management strategies,ultimately aiming to improve patient outcomes and reduce the global burden of DR.The integration of AI and ML in DR care represents a paradigm shift,offering a glimpse into the future of ophthalmic healthcare.

Recognition and quality mapping of traditional herbal drugs:way forward towards artificial intelligence

The use of traditional herbal drugs derived from natural sources is on the rise due to their minimal side effects and numerous health benefits.However,a major limitation is the lack of standardized knowledge for identifying and mapping the quality of these herbal medicines.This article aims to provide practical insights into the application of artificial intelligence for quality-based commercialization of raw herbal drugs.It focuses on feature extraction methods,image processing techniques,and the preparation of herbal images for compatibility with machine learning models.The article discusses commonly used image processing tools such as normalization,slicing,cropping,and augmentation to prepare images for artificial intelligence-based models.It also provides an overview of global herbal image databases and the models employed for herbal plant/drug identification.Readers will gain a comprehensive understanding of the potential application of various machine learning models,including artificial neural networks and convolutional neural networks.The article delves into suitable validation parameters like true positive rates,accuracy,precision,and more for the development of artificial intelligence-based identification and authentication techniques for herbal drugs.This article offers valuable insights and a conclusive platform for the further exploration of artificial intelligence in the field of herbal drugs,paving the way for smarter identification and authentication methods.

Gallbladder carcinoma in the era of artificial intelligence: Early diagnosis for better treatment

Gallbladder carcinoma(GBC)is the most common malignant tumor of biliary tract,with poor prognosis due to its aggressive nature and limited therapeutic options.Early detection of GBC is a major challenge,with most GBCs being detected accidentally during cholecystectomy procedures for gallbladder stones.This letter comments on the recent article by Deqing et al in the World Journal of Gastrointestinal Oncology,which summarized the various current methods used in early diagnosis of GBC,including endoscopic ultrasound(EUS)examination of the gallbladder for high-risk GBC patients,and the use of EUS-guided elasto-graphy,contrast-enhanced EUS,trans-papillary biopsy,natural orifice translu-minal endoscopic surgery,magnifying endoscopy,choledochoscopy,and confocal laser endomicroscopy when necessary for early diagnosis of GBC.However,there is a need for novel methods for early GBC diagnosis,such as the use of artificial intelligence and non-coding RNA biomarkers for improved screening protocols.Additionally,the use of in vitro and animal models may provide critical insights for advancing early detection and treatment strategies of this aggressive tumor.

Harnessing artificial intelligence for identifying conflicts of interest in research

This editorial explores the transformative potential of artificial intelligence(AI)in identifying conflicts of interest(COIs)within academic and scientific research.By harnessing advanced data analysis,pattern recognition,and natural language processing techniques,AI offers innovative solutions for enhancing transparency and integrity in research.This editorial discusses how AI can automatically detect COIs,integrate data from various sources,and streamline reporting processes,thereby maintaining the credibility of scientific findings.

Ultraconformable Integrated Wireless Charging Micro-Supercapacitor Skin

Conformable and wire-less charging energy storage devices play important roles in enabling the fast development of wearable,non-contact soft electronics.However,current wire-less charging power sources are still restricted by limited flexural angles and fragile connection of components,resulting in the failure expression of performance and constraining their fur-ther applications in health monitoring wearables and moveable artificial limbs.Herein,we present an ultracompatible skin-like integrated wireless charging micro-supercapacitor,which building blocks(including electrolyte,electrode and substrate)are all evaporated by liquid precursor.Owing to the infiltration and permeation of the liquid,each part of the integrated device attached firmly with each other,forming a compact and all-in-one configuration.In addition,benefitting from the controllable volume of electrode solution precursor,the electrode thickness is easily regulated varying from 11.7 to 112.5μm.This prepared thin IWC-MSC skin can fit well with curving human body,and could be wireless charged to store electricity into high capacitive micro-supercapacitors(11.39 F cm-3)of the integrated device.We believe this work will shed light on the construction of skin-attachable electronics and irregular sensing microrobots.

Artificial dermis combined with skin grafting for the treatment of hand skin and soft tissue defects and exposure of bone and tendon

BACKGROUND The recovery time of hand wounds is long,which can easily result in chronic and refractory wounds,making the wounds unable to be properly repaired.The treatment cycle is long,the cost is high,and it is prone to recurrence and disability.Double layer artificial dermis combined with autologous skin transplantation has been used to repair hypertrophic scars,deep burn wounds,exposed bone and tendon wounds,and post tumor wounds.AIM To investigate the therapeutic efficacy of autologous skin graft transplantation in conjunction with double-layer artificial dermis in treating finger skin wounds that are chronically refractory and soft tissue defects that expose bone and tendon.METHODS Sixty-eight chronic refractory patients with finger skin and soft tissue defects accompanied by bone and tendon exposure who were admitted from July 2021 to June 2022 were included in this study.The observation group was treated with double layer artificial dermis combined with autologous skin graft transplantation(n=49),while the control group was treated with pedicle skin flap transplantation(n=17).The treatment status of the two groups of patients was compared,including the time between surgeries and hospital stay.The survival rate of skin grafts/flaps and postoperative wound infections were evaluated using the Vancouver Scar Scale(VSS)for scar scoring at 6 mo after surgery,as well as the sensory injury grading method and two-point resolution test to assess the recovery of skin sensation at 6 mo.The satisfaction of the two groups of patients was also compared.RESULTS Wound healing time in the observation group was significantly longer than that in the control group(P<0.05,27.92±3.25 d vs 19.68±6.91 d);there was no significant difference in the survival rate of skin grafts/flaps between the two patient groups(P>0.05,95.1±5.0 vs 96.3±5.6).The interval between two surgeries(20.0±4.3 d)and hospital stay(21.0±10.1 d)in the observation group were both significantly shorter than those in the control group(27.5±9.3 d)and(28.4±17.7 d),respectively(P<0.05).In comparison to postoperative infection(23.5%)and subcutaneous hematoma(11.8%)in the control group,these were considerably lower at(10.2%)and(6.1%)in the observation group.When comparing the two patient groups at six months post-surgery,the excellent and good rate of sensory recovery(91.8%)was significantly higher in the observation group than in the control group(76.5%)(P<0.05).There was also no statistically significant difference in two point resolution(P>0.05).The VSS score in the observation group(2.91±1.36)was significantly lower than that in the control group(5.96±1.51),and group satisfaction was significantly higher(P<0.05,90.1±6.3 vs 76.3±5.2).CONCLUSION The combination of artificial dermis and autologous skin grafting for the treatment of hand tendon exposure wounds has a satisfactory therapeutic effect.It is a safe,effective,and easy to operate treatment method,which is worthy of clinical promotion.

Multistage Microstructured Ionic Skin for Real-Time Vital Signs Monitoring and Human-Machine Interaction

Skin-like electronics research aiming to mimic even surpass human-like specific tactile cognition by operating perception-to-cognition-to-feedback of stimulus to build intelligent cognition systems for certain imperceptible or inappreciable signals was so attractive.Herein,we constructed an all-in-one tri-modal pressure sensing wearable device to address the issue of power supply by integrating multistage microstructured ionic skin(MM i-skin)and thermoelectric self-power staffs,which exhibits high sensitivity simultaneously.The MM i-skin with multi-stage“interlocked”configurations achieved precise recognition of subtle signals,where the sensitivity reached up to 3.95 kPa^(−1),as well as response time of 46 ms,cyclic stability(over 1500 cycles),a wide detection range of 0–200 kPa.Furthermore,we developed the thermoelectricity nanogenerator,piezoelectricity nanogenerator,and piezocapacitive sensing as an integrated tri-modal pressure sensing,denoted as P-iskin,T-iskin,and C-iskin,respectively.This multifunctional ionic skin enables real-time monitoring of weak body signals,rehab guidance,and robotic motion recognition,demonstrating potential for Internet of things(IoT)applications involving the artificial intelligence-motivated sapiential healthcare Internet(SHI)and widely distributed human-machine interaction(HMI).

Bioengineered skin organoids:from development to applications

Signifcant advancements have been made in recent years in the development of highly sophisticated skin organoids.Serving as three-dimensional(3D)models that mimic human skin,these organoids have evolved into complex structures and are increasingly recognized as efective alternatives to traditional culture models and human skin due to their ability to overcome the limitations of two-dimensional(2D)systems and ethical concerns.The inherent plasticity of skin organoids allows for their construction into physiological and pathological models,enabling the study of skin development and dynamic changes.This review provides an overview of the pivotal work in the progression from 3D layered epidermis to cyst-like skin organoids with appendages.Furthermore,it highlights the latest advancements in organoid construction facilitated by state-of-the-art engineering techniques,such as 3D printing and microfuidic devices.The review also summarizes and discusses the diverse applications of skin organoids in developmental biology,disease modelling,regenerative medicine,and personalized medicine,while considering their prospects and limitations.

Regulatory T cells in skin regeneration and wound healing

As the body’s integumentary system,the skin is vulnerable to injuries.The subsequent wound healing processes aim to restore dermal and epidermal integrity and functionality.To this end,multiple tissue-resident cells and recruited immune cells cooperate to efficiently repair the injured tissue.Such temporally-and spatially-coordinated interplay necessitates tight regulation to prevent collateral damage such as overshooting immune responses and excessive inflammation.In this context,regulatory T cells(Tregs)hold a key role in balancing immune homeostasis and mediating cutaneous wound healing.A comprehensive understanding of Tregs’multifaceted field of activity may help decipher wound pathologies and,ultimately,establish new treatment modalities.Herein,we review the role of Tregs in orchestrating the regeneration of skin adnexa and catalyzing healthy wound repair.Further,we discuss how Tregs operate during fibrosis,keloidosis,and scarring.

Skin Characteristics of Sites Predisposed to Pressure Ulcers among Bedridden Elderly Patients in Japan

Background: Previous studies have not specifically measured skin characteristics at common sites of pressure ulcers in high-risk elderly patients. Therefore, this study aimed to clarify the physiological skin characteristics at common pressure ulcer sites and their relationship with pressure ulcer risk and demographic/laboratory data in bedridden elderly Japanese patients. Methods: This study involved 55 elderly Japanese patients in a long-term care hospital and 25 female Japanese university students. Skin surface temperature, epidermal water content, transepidermal water loss, skin erythema/redness, skin elasticity, and skin thickness were measured using noninvasive devices. The sacral and both heel areas (sites predisposed to pressure ulcers) and mid-to-lower back area (control site) were observed. Results: Elderly patients showed a low epidermal water content at both heels (right heel, 14.8 ± 9.1 arbitrary units, AU.;left heel, 14.4 ± 8.3 AU). Transepidermal water loss remained acceptable at all sites in older patients despite the presence of dry skin (back, 7.1 ± 1.8 g/hm2;sacrum, 7.4 ± 3.0 g/hm2;right heel, 17.7 ± 7.1 g/hm2;left heel, 19.4 ± 8.1 g/hm2). Back (0.61 ± 0.13 AU) and sacral (0.67 ± 0.11 AU) skin elasticity and sacral skin thickness (0.97 ± 0.56 cm) were significantly lower than those of healthy young people (0.86 ± 0.04 AU, 0.87 ± 0.05 AU, and 2.27 ± 0.84 cm, respectively;p < 0.001 for all sites). Moderate positive correlations were observed between back skin elasticity and serum albumin level (r = 0.445, p < 0.001), and between sacral skin thickness and BMI (r = 0.506, p < 0.001) in older patients. Conclusions: Our findings showed that skin thickness and elasticity should be prioritized when evaluating pressure ulcer risk at the sacral region in bedridden elderly Japanese patients. Moreover, skin moisturization should be considered to minimize the risk at the heels in such patients.