Artificial Intelligence in Dermatology-Where We Are and the Way to the Future: A Review

The optimal labelling method for artificial intelligence-assisted polyp detection in colonoscopy

BACKGROUND

The methodology in colon polyp labeling in establishing database for ma-chine learning is not well-described and standardized. We aimed to find out the best annotation method to generate the most accurate model in polyp detection.

METHODS

3542 colonoscopy polyp images were obtained from endoscopy database of a tertiary medical center. Two experienced endoscopists manually annotated the polyp with (1) exact outline segmentation and (2) using a standard rectangle box close to the polyp margin, and extending 10%, 20%, 30%, 40% and 50% longer in both width and length of the standard rectangle for AI modeling setup. The images were randomly divided into training and validation sets in 4:1 ratio. U-Net convolutional network architecture was used to develop automatic segmentation machine learning model. Another unrelated verification set was established to evaluate the performance of polyp detection by different segmentation methods.

RESULTS

Extending the bounding box to 20% of the polyp margin represented the best performance in accuracy (95.42%), sensitivity (94.84%) and F1-score (95.41%). Exact outline segmentation model showed the excellent performance in sensitivity (99.6%) and the worst precision (77.47%). The 20% model was the best among the 6 models. (confidence interval = 0.957-0.985; AUC = 0.971).

CONCLUSIONS

Labelling methodology affect the predictability of AI model in polyp detection. Extending the bounding box to 20% of the polyp margin would result in the best polyp detection predictive model based on AUC data. It is mandatory to establish a standardized way in colon polyp labeling for comparison of the precision of different AI models.

The National Health Service 2-week-wait skin cancer referral pathway: analysis and recommendations for process improvement

BACKGROUND

Although pressures on the National Health Service skin cancer services pre-dated the COVID pandemic, the 2-week-wait (2WW) skin cancer standards that require hospitals to provide an appointment with a specialist within 2 weeks for anyone with symptoms that may indicate cancer have deteriorated post the pandemic.

OBJECTIVES

To undertake process mapping of the current 2WW skin cancer pathways and provide recommendations to streamline and improve the pathway to meet the 2WW standards.

METHODS

The 2WW skin cancer pathway is analysed in this report utilizing 4 V typology encompassing the four dimensions of operations: volume, variety, variation and visibility, and a volume-variety matrix. A performance matrix of the 2WW skin cancer pathway and SIPOC (suppliers, inputs, processes, outputs and customers) analysis are also examined.

RESULTS

Process analysis enabled the identification of some of the limitations of the 2WW skin cancer pathway. Following analysis and redesign of the process map of the pathway, recommendations, including lesion assessment using artificial intelligence, single-lesion assessment clinics and direct access skin surgery, all of which aim to expedite patient care and increase capacity in 2WW clinics, are provided.

CONCLUSIONS

The process analysis of the 2WW skin cancer pathway provides useful insights and helps identify bottlenecks in the system. Recommendations following remapping the process offer potential solutions to help reduce time to referral and to increase capacity. These recommendations should help reduce waiting times for patients receiving an initial diagnosis and subsequent definitive treatment for suspected skin cancers.

Artificial Intelligence in the Non-Invasive Detection of Melanoma

Skin cancer is one of the most prevalent cancers worldwide, with increasing incidence. Skin cancer is typically classified as melanoma or non-melanoma skin cancer. Although melanoma is less common than basal or squamous cell carcinomas, it is the deadliest form of cancer, with nearly 8300 Americans expected to die from it each year. Biopsies are currently the gold standard in diagnosing melanoma; however, they can be invasive, expensive, and inaccessible to lower-income individuals. Currently, suspicious lesions are triaged with image-based technologies, such as dermoscopy and confocal microscopy. While these techniques are useful, there is wide inter-user variability and minimal training for dermatology residents on how to properly use these devices. The use of artificial intelligence (AI)-based technologies in dermatology has emerged in recent years to assist in the diagnosis of melanoma that may be more accessible to all patients and more accurate than current methods of screening. This review explores the current status of the application of AI-based algorithms in the detection of melanoma, underscoring its potential to aid dermatologists in clinical practice. We specifically focus on AI application in clinical imaging, dermoscopic evaluation, algorithms that can distinguish melanoma from non-melanoma skin cancers, and in vivo skin imaging devices.

Web application for assisting non-dermatology physicians in learning and managing patients with common cutaneous adverse drug reactions: a multicenter randomized controlled trial

BACKGROUND

Cutaneous adverse drug reactions (CADRs) remain a challenge for non-dermatologists. Medical-related applications to assist in learning about and managing patients with CADRs are scarce. We aimed to evaluate the efficacy of a web application for non-dermatologists in managing CADRs by comparing the knowledge scores of users and non-users.

MATERIALS AND METHODS

A multicenter randomized controlled trial was conducted between January 2023 and May 2023. Clinician participants were randomized (1:1) into the application and control groups using a simple randomization method. Knowledge scores between the groups were compared to evaluate the efficacy of the web application, and participants' perspectives on the application were also collected.

RESULTS

A total of 44 clinician participants were included in the final analysis. The median age was 33.0 years (95% confidence interval (CI) 27.5-35.0) and predominantly female (56.8%). The score in the application group (median, 27.0; 95% CI, 25.0-28.0) was significantly higher than that in the control group (median, 14.0; 95% CI 13.0-17.0) (p < 0.001). There were no differences in scores between the sex groups (p = 0.695), between general practitioners (GPs) and non-GPs (p = 0.93), or among groups with different frequencies of evaluation of patients with CADRs (p = 0.266). In addition, the participants in the application group rated a high level of overall satisfaction.

CONCLUSION

The web application for CADRs is an effective and convenient tool for assisting non-dermatologist physicians in learning and providing initial management with a high level of satisfaction. However, prospective long-term randomized controlled studies are required to confirm the efficacy of this tool.

Diagnostic stewardship and dermatology consultation in cellulitis management: a systematic literature review and meta-analysis

Cellulitis is a common skin infection often requiring antibiotic treatment. However, misdiagnosis and inappropriate antibiotic use contribute to antibiotic resistance and healthcare costs. We aimed to evaluate the impact of dermatology consultation on treatment modification in patients with suspected cellulitis and to determine whether dermatologists' evaluation can be used as a reference to diagnose suspected cellulitis. We explored MedLine (PubMed), Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane CENTRAL, Web of Science, and Scopus and Embase, including publications from database inception to July 25, 2023. Studies were included if they evaluated treatment modifications involving the use of antibiotics for patients with suspected cellulitis with and without dermatology consultation. We excluded comments or reviews, pilot studies, and studies without a non-dermatology control group, treatment modifications, the use of antibiotics, or patients with cellulitis. Of the 49 full-text articles, 14 studies met the selection criteria.This systematic literature review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement and Meta-analysis of Observational Studies in Epidemiology (MOOSE) guidelines. Of five independent reviewers (GYC, IP, MG, MKH, and AMRP), two abstracted data for each article using a standardized abstraction form. We used the Downs and Black scale to evaluate study quality. Subgroup analysis was conducted regarding readmission rate within 30 days for two independent populations seen by a non-dermatologist physician or a dermatologist. We employed random-effects models to obtain pooled mean differences. Heterogeneity was assessed using the I-squared value. The impact of dermatology consultation on treatment modification involving antibiotics in patients with suspected cellulitis and readmission rates in 30 days. Dermatology consultation changed initial treatment plans involving antibiotics from 47 to 96% of the time, improving diagnostic accuracy and, consequently, antibiotic stewardship of cellulitis. Dermatology consultation was not significantly associated with lower readmission rates in 30 days (pooled OR = 0.56, 95% CI: 0.25 to 1.25, I2 = 0%). Dermatology consultation in patients with suspected cellulitis may improve diagnosis and management, thereby reducing antibiotic misuse, unnecessary tests, and prolonged hospitalizations.

Image-Based Artificial Intelligence in Psoriasis Assessment: The Beginning of a New Diagnostic Era?

Psoriasis, a chronic inflammatory skin disease, affects millions of people worldwide. It imposes a significant burden on patients' quality of life and healthcare systems, creating an urgent need for optimized diagnosis, treatment, and management. In recent years, image-based artificial intelligence (AI) applications have emerged as promising tools to assist physicians by offering improved accuracy and efficiency. In this review, we provide an overview of the current landscape of image-based AI applications in psoriasis. Emphasis is placed on machine learning (ML) algorithms, a key subset of AI, which enable automated pattern recognition for various tasks. Key AI applications in psoriasis include lesion detection and segmentation, differentiation from other skin conditions, subtype identification, automated area involvement, and severity scoring, as well as personalized treatment selection and response prediction. Furthermore, we discuss two commercially available systems that utilize standardized photo documentation, automated segmentation, and semi-automated Psoriasis Area and Severity Index (PASI) calculation for patient assessment and follow-up. Despite the promise of AI in this field, many challenges remain. These include the validation of current models, integration into clinical workflows, the current lack of diversity in training-set data, and the need for standardized imaging protocols. Addressing these issues is crucial for the successful implementation of AI technologies in clinical practice. Overall, we underscore the potential of AI to revolutionize psoriasis management, highlighting both the advancements and the hurdles that need to be overcome. As technology continues to evolve, AI is expected to significantly improve the accuracy, efficiency, and personalization of psoriasis treatment.

Medical students and house officers' perception, attitude and potential barriers towards artificial intelligence in Egypt, cross sectional survey

BACKGROUND

Artificial intelligence (AI) is one of the sectors of medical research that is expanding the fastest right now in healthcare. AI has rapidly advanced in the field of medicine, helping to treat a variety of illnesses and reducing the number of diagnostic and follow-up errors.

OBJECTIVE

This study aims to assess the perception and attitude towards artificial intelligence (AI) among medical students & house officers in Egypt.

METHODS

An online cross-sectional study was done using a questionnaire on the Google Form website. The survey collected demographic data and explored participants' perception, attitude & potential barriers towards AI.

RESULTS

There are 1,346 responses from Egyptian medical students (25.8%) & house officers (74.2%). Most participants have inadequate perception (76.4%) about the importance and usage of AI in the medical field, while the majority (87.4%) have a negative attitude. Multivariate analysis revealed that age is the only independent predictor of AI perception (AOR = 1.07, 95% CI 1.01-1.13). However, perception level and gender are both independent predictors of attitude towards AI (AOR = 1.93, 95% CI 1.37-2.74 & AOR = 1.80, 95% CI 1.30-2.49, respectively).

CONCLUSION

The study found that medical students and house officers in Egypt have an overall negative attitude towards the integration of AI technologies in healthcare. Despite the potential benefits of AI-driven digital medicine, most respondents expressed concerns about the practical application of these technologies in the clinical setting. The current study highlights the need to address the concerns of medical students and house officers towards AI integration in Egypt. A multi-pronged approach, including education, targeted training, and addressing specific concerns, is necessary to facilitate the wider adoption of AI-enabled healthcare.

AI in Psoriatic Disease: Scoping Review

BACKGROUND

Artificial intelligence (AI) has many applications in numerous medical fields, including dermatology. Although the majority of AI studies in dermatology focus on skin cancer, there is growing interest in the applicability of AI models in inflammatory diseases, such as psoriasis. Psoriatic disease is a chronic, inflammatory, immune-mediated systemic condition with multiple comorbidities and a significant impact on patients' quality of life. Advanced treatments, including biologics and small molecules, have transformed the management of psoriatic disease. Nevertheless, there are still considerable unmet needs. Globally, delays in the diagnosis of the disease and its severity are common due to poor access to health care systems. Moreover, despite the abundance of treatments, we are unable to predict which is the right medication for the right patient, especially in resource-limited settings. AI could be an additional tool to address those needs. In this way, we can improve rates of diagnosis, accurately assess severity, and predict outcomes of treatment.

OBJECTIVE

This study aims to provide an up-to-date literature review on the use of AI in psoriatic disease, including diagnostics and clinical management as well as addressing the limitations in applicability.

METHODS

We searched the databases MEDLINE, PubMed, and Embase using the keywords "AI AND psoriasis OR psoriatic arthritis OR psoriatic disease," "machine learning AND psoriasis OR psoriatic arthritis OR psoriatic disease," and "prognostic model AND psoriasis OR psoriatic arthritis OR psoriatic disease" until June 1, 2023. Reference lists of relevant papers were also cross-examined for other papers not detected in the initial search.

RESULTS

Our literature search yielded 38 relevant papers. AI has been identified as a key component in digital health technologies. Within this field, there is the potential to apply specific techniques such as machine learning and deep learning to address several aspects of managing psoriatic disease. This includes diagnosis, particularly useful for remote teledermatology via photographs taken by patients as well as monitoring and estimating severity. Similarly, AI can be used to synthesize the vast data sets already in place through patient registries which can help identify appropriate biologic treatments for future cohorts and those individuals most likely to develop complications.

CONCLUSIONS

There are multiple advantageous uses for AI and digital health technologies in psoriatic disease. With wider implementation of AI, we need to be mindful of potential limitations, such as validation and standardization or generalizability of results in specific populations, such as patients with darker skin phototypes.

Artificial intelligence for nonmelanoma skin cancer

Nonmelanoma skin cancers (NMSCs) are among the top five most common cancers globally. NMSC is an area with great potential for novel application of diagnostic tools including artificial intelligence (AI). In this scoping review, we aimed to describe the applications of AI in the diagnosis and treatment of NMSC. Twenty-nine publications described AI applications to dermatopathology including lesion classification and margin assessment. Twenty-five publications discussed AI use in clinical image analysis, showing that algorithms are not superior to dermatologists and may rely on unbalanced, nonrepresentative, and nontransparent training data sets. Sixteen publications described the use of AI in cutaneous surgery for NMSC including use in margin assessment during excisions and Mohs surgery, as well as predicting procedural complexity. Eleven publications discussed spectroscopy, confocal microscopy, thermography, and the AI algorithms that analyze and interpret their data. Ten publications pertained to AI applications for the discovery and use of NMSC biomarkers. Eight publications discussed the use of smartphones and AI, specifically how they enable clinicians and patients to have increased access to instant dermatologic assessments but with varying accuracies. Five publications discussed large language models and NMSC, including how they may facilitate or hinder patient education and medical decision-making. Three publications pertaining to the skin of color and AI for NMSC discussed concerns regarding limited diverse data sets for the training of convolutional neural networks. AI demonstrates tremendous potential to improve diagnosis, patient and clinician education, and management of NMSC. Despite excitement regarding AI, data sets are often not transparently reported, may include low-quality images, and may not include diverse skin types, limiting generalizability. AI may serve as a tool to increase access to dermatology services for patients in rural areas and save health care dollars. These benefits can only be achieved, however, with consideration of potential ethical costs.

Patient-led skin cancer teledermatology without dermoscopy during the COVID-19 pandemic: important lessons for the development of future patient-facing teledermatology and artificial intelligence-assisted -self-diagnosis

MySkinSelfie is a mobile phone application for skin self-monitoring, enabling secure sharing of patient-captured images with healthcare providers. This retrospective study assessed MySkinSelfie's role in remote skin cancer assessment at two centres for urgent (melanoma and squamous cell carcinoma) and nonurgent skin cancer referrals, investigating the feasibility of using patient-captured images without dermoscopy for remote diagnosis. The total number of lesions using MySkinSelfie was 814, with a mean patient age of 63 years. Remote consultations reduced face-to-face appointments by 90% for basal cell carcinoma and by 63% for referrals on a 2-week waiting list. Diagnostic concordance (consultant vs. histological diagnosis) rates of 72% and 83% were observed for basal cell carcinoma (n = 107) and urgent skin cancers (n = 704), respectively. Challenges included image quality, workflow integration and lack of dermoscopy. Higher sensitivities were observed in recent artificial intelligence algorithms employing dermoscopy. While patient-captured images proved useful during the COVID-19 pandemic, further research is needed to explore the feasibility of widespread patient-led dermoscopy to enable direct patient-to-artificial intelligence diagnostic assessment.

Applicability and benefits of telemedicine in the monitoring of monkeypox close contacts

Monkeypox is a zoonotic disease caused by the monkeypox virus and classically presents as a vesicular rash accompanied by fever and lymphadenopathy. Singapore reported the first imported case of monkeypox infection on 21 June 2022, the first local unlinked case on 6 July 2022, and the first local linked case on 5 August 2022. Telemedicine was used in the management of monkeypox close contacts to (1) screen for the development of signs and symptoms consistent with monkeypox infection, (2) assess for successful post-exposure prophylaxis via direct visualisation of vaccination site morphological progression, (3) detect serious reactions arising from post-exposure prophylaxis administration, and (4) evaluate for deterioration in mental health status during the 21-day quarantine period. A case series of 13 close contacts who received post-exposure prophylaxis in the form of the ACAM2000 live Vaccinia virus preparation is presented, illustrating the safe and efficacious application of telemedicine in the clinical follow-up of quarantined close contacts throughout the 21-day incubation period, and post-exposure prophylaxis monitoring. Inherent limitations included difficulties in the assessment of sensitive areas such as the peri-genital and peri-anal regions and video quality-related issues.

Optimizing time prediction and error classification in early melanoma detection using a hybrid RCNN-LSTM model

Skin cancer is a terrifying disorder that affects all individuals. Due to the significant increase in the rate of melanoma skin cancer, early detection of skin cancer is now more critical than ever before. Malignant melanoma is one of the most serious forms of skin cancer, and it is caused by abnormal melanocyte cell growth. In recent years, skin cancer predictive categorization has become more accurate and predictive due to multiple deep learning algorithms. Malignant melanoma is diagnosed using the Recurrent Convolution Neural Network-Long Short-Term Memory (RCNN-LSTM), which is one of the deep learning classification approaches. Using the International Skin Image Collection and the RCNN-LSTM, the data are categorized and analyzed to gain a better understanding of skin cancer. The method begins with data preprocessing, which prepares the dataset for classification. Additionally, the RCNN is employed to extract the features that are vital to the prediction process. The LSTM is accountable for the final step, classification. There are further factors to examine, such as the precision of 94.60%, the sensitivity of 95.67%, and the F1-score of 95.13%. Other benefits of the suggested study include shorter prediction durations of 95.314, 122.530, and 131.205 s and lower model loss of 0.25%, 0.19%, and 0.15% for input sizes 10, 15, and 20, respectively. Three datasets had a reduced categorization error of 5.11% and an accuracy of 95.42%. In comparison to previous approaches, the work discussed here produces superior outcomes. RESEARCH HIGHLIGHTS: Recurrent convolutional neural network (RCNN) deep learning approach for optimizing time prediction and error classification in early melanoma detection. It extracts a high number of specific features from the skin disease image, making the classification process easier and more accurate. To reduce classification errors in accurately detecting melanoma, context dependency is considered in this work. By accounting for context dependency, the deprivation state is avoided, preventing performance degradation in the model. To minimize melanoma detection model loss, a skin disease image augmentation or regularization process is performed in this work. This strategy improves the accuracy of the model when applied to fresh, previously unobserved data.

Technology innovation to reduce health inequality in skin diagnosis and to improve patient outcomes for people of color: a thematic literature review and future research agenda

The health inequalities experienced by ethnic minorities have been a persistent and global phenomenon. The diagnosis of different types of skin conditions, e.g., melanoma, among people of color is one of such health domains where misdiagnosis can take place, potentially leading to life-threatening consequences. Although Caucasians are more likely to be diagnosed with melanoma, African Americans are four times more likely to present stage IV melanoma due to delayed diagnosis. It is essential to recognize that additional factors such as socioeconomic status and limited access to healthcare services can be contributing factors. African Americans are also 1.5 times more likely to die from melanoma than Caucasians, with 5-year survival rates for African Americans significantly lower than for Caucasians (72.2% vs. 89.6%). This is a complex problem compounded by several factors: ill-prepared medical practitioners, lack of awareness of melanoma and other skin conditions among people of colour, lack of information and medical resources for practitioners' continuous development, under-representation of people of colour in research, POC being a notoriously hard to reach group, and 'whitewashed' medical school curricula. Whilst digital technology can bring new hope for the reduction of health inequality, the deployment of artificial intelligence in healthcare carries risks that may amplify the health disparities experienced by people of color, whilst digital technology may provide a false sense of participation. For instance, Derm Assist, a skin diagnosis phone application which is under development, has already been criticized for relying on data from a limited number of people of color. This paper focuses on understanding the problem of misdiagnosing skin conditions in people of color and exploring the progress and innovations that have been experimented with, to pave the way to the possible application of big data analytics, artificial intelligence, and user-centred technology to reduce health inequalities among people of color.

Ethical considerations for artificial intelligence in dermatology: a scoping review

The field of dermatology is experiencing the rapid deployment of artificial intelligence (AI), from mobile applications (apps) for skin cancer detection to large language models like ChatGPT that can answer generalist or specialist questions about skin diagnoses. With these new applications, ethical concerns have emerged. In this scoping review, we aimed to identify the applications of AI to the field of dermatology and to understand their ethical implications. We used a multifaceted search approach, searching PubMed, MEDLINE, Cochrane Library and Google Scholar for primary literature, following the PRISMA Extension for Scoping Reviews guidance. Our advanced query included terms related to dermatology, AI and ethical considerations. Our search yielded 202 papers. After initial screening, 68 studies were included. Thirty-two were related to clinical image analysis and raised ethical concerns for misdiagnosis, data security, privacy violations and replacement of dermatologist jobs. Seventeen discussed limited skin of colour representation in datasets leading to potential misdiagnosis in the general population. Nine articles about teledermatology raised ethical concerns, including the exacerbation of health disparities, lack of standardized regulations, informed consent for AI use and privacy challenges. Seven addressed inaccuracies in the responses of large language models. Seven examined attitudes toward and trust in AI, with most patients requesting supplemental assessment by a physician to ensure reliability and accountability. Benefits of AI integration into clinical practice include increased patient access, improved clinical decision-making, efficiency and many others. However, safeguards must be put in place to ensure the ethical application of AI.

Challenges of artificial intelligence in medicine and dermatology

Artificial intelligence (AI) in medicine and dermatology brings additional challenges related to bias, transparency, ethics, security, and inequality. Bias in AI algorithms can arise from biased training data or decision-making processes, leading to disparities in health care outcomes. Addressing bias requires careful examination of the data used to train AI models and implementation of strategies to mitigate bias during algorithm development. Transparency is another critical challenge, as AI systems often operate as black boxes, making it difficult to understand how decisions are reached. Ensuring transparency in AI algorithms is vital to gaining trust from both patients and health care providers. Ethical considerations arise when using AI in health care, including issues such as informed consent, privacy, and the responsibility for the decisions made by AI systems. It is essential to establish clear guidelines and frameworks that govern the ethical use of AI, including maintaining patient autonomy and protecting sensitive health information. Security is a significant concern in AI systems, as they rely on vast amounts of sensitive patient data. Protecting these data from unauthorized access, breaches, or malicious attacks is paramount to maintaining patient privacy and trust in AI technologies. Lastly, the potential for inequality arises if AI technologies are not accessible to all populations, leading to a digital divide in health care. Efforts should be made to ensure that AI solutions are affordable, accessible, and tailored to the needs of diverse communities, mitigating the risk of exacerbating existing health care disparities. Addressing these challenges is crucial for AI's responsible and equitable integration in medicine and dermatology.

Ethics of artificial intelligence in dermatology

The integration of artificial intelligence (AI) in dermatology holds promise for enhancing clinical accuracy, enabling earlier detection of skin malignancies, suggesting potential management of skin lesions and eruptions, and promoting improved continuity of care. AI implementation in dermatology, however, raises several ethical concerns. This review explores the current benefits and challenges associated with AI integration, underscoring ethical considerations related to autonomy, informed consent, and privacy. We also examine the ways in which beneficence, nonmaleficence, and distributive justice may be impacted. Clarifying the role of AI, striking a balance between security and transparency, fostering open dialogue with our patients, collaborating with developers of AI, implementing educational initiatives for dermatologists and their patients, and participating in the establishment of regulatory guidelines are essential to navigating ethical and responsible AI incorporation into dermatology.

Artificial Intelligence: A Snapshot of Its Application in Chronic Inflammatory and Autoimmune Skin Diseases

Immuno-correlated dermatological pathologies refer to skin disorders that are closely associated with immune system dysfunction or abnormal immune responses. Advancements in the field of artificial intelligence (AI) have shown promise in enhancing the diagnosis, management, and assessment of immuno-correlated dermatological pathologies. This intersection of dermatology and immunology plays a pivotal role in comprehending and addressing complex skin disorders with immune system involvement. The paper explores the knowledge known so far and the evolution and achievements of AI in diagnosis; discusses segmentation and the classification of medical images; and reviews existing challenges, in immunological-related skin diseases. From our review, the role of AI has emerged, especially in the analysis of images for both diagnostic and severity assessment purposes. Furthermore, the possibility of predicting patients' response to therapies is emerging, in order to create tailored therapies.

Recent Research Trends against Skin Carcinoma - An Overview

Skin cancer is a prevalent and sometimes lethal cancer that affects a wide range of people. UV radiation exposure is the main cause of skin cancer. Immunosuppression, environmental factors, and genetic predisposition are other contributing variables. Fair-skinned people and those with a history of sunburns or severe sun exposure are more likely to experience this condition. Melanoma, squamous cell carcinoma (SCC), and basal cell carcinoma (BCC) are the three main forms. Melanoma poses a bigger hazard because of its tendency for metastasis, while SCC and BCC have limited metastatic potential. Genetic mutations and changes to signalling pathways such as p53 and MAPK are involved in pathogenesis. Early diagnosis is essential, and molecular testing, biopsy, dermoscopy, and visual inspection can all help. In addition to natural medicines like curcumin and green tea polyphenols, treatment options include immunotherapy, targeted therapy, radiation, surgery, and chemotherapy. Reducing the incidence of skin cancer requires preventive actions, including sun protection and early detection programs. An overview of skin cancers, including their forms, pathophysiology, diagnosis, and treatment, highlighting herbal therapy, is given in this review.

Embedding artificial intelligence in healthcare: An ethnographic exploration of an AI-based mHealth app through the lens of legitimacy

Purpose

Skin cancer, a significant global health problem, imposes financial and workload burdens on the Dutch healthcare system. Artificial intelligence (AI) for diagnostic augmentation has gained momentum in dermatology, but despite significant research on adoption, acceptance, and implementation, we lack a holistic understanding of why technologies (do not) become embedded in the healthcare system. This study utilizes the concept of legitimacy, omnipresent but underexplored in health technology studies, to examine assumptions guiding the integration of an AI mHealth app for skin lesion cancer risk assessment in the Dutch healthcare system.

Methods

We conducted a 3-year ethnographic case study, using participant observation, interviews, focus groups, and document analysis to explore app integration within the Dutch healthcare system. Participants included doctors, policymakers, app users, developers, insurers, and researchers. Our analysis focused on moments of legitimacy breakdown, contrasting company narratives and healthcare-based assumptions with practices and affectively-charged experiences of professionals and app users.

Results

Three major kinds of legitimacy breakdowns impacted the embedding of this app: first, lack of institutional legitimacy led to informal workarounds by the company at the institutional level; second, quantification privilege impacted app influence in interactions with doctors; and third, interactive limits between users and the app contradicted expectations around ease of use and work burden alleviation.

Conclusions

Our results demonstrate that legitimacy is a useful lens for understanding the embedding of health technology while taking into account institutional complexity. A legitimacy lens is helpful for decision-makers and researchers because it can clarify and anticipate pain points for the integration of AI into healthcare systems.

The Role of Artificial Intelligence and Machine Learning in Assisted Reproductive Technologies

Artificial intelligence (AI) and machine learning, the form most commonly used in medicine, offer powerful tools utilizing the strengths of large data sets and intelligent algorithms. These systems can help to revolutionize delivery of treatments, access to medical care, and improvement of outcomes, particularly in the realm of reproductive medicine. Whether that is more robust oocyte and embryo grading or more accurate follicular measurement, AI will be able to aid clinicians, and most importantly patients, in providing the best possible and individualized care. However, despite all of the potential strengths of AI, algorithms are not immune to bias and are vulnerable to the many socioeconomic and demographic biases that current healthcare systems suffer from. Wrong diagnoses as well is furthering of healthcare discrimination are real possibilities if both the capabilities and limitations of AI are not well understood. Armed with appropriate knowledge of how AI can most appropriately operate within medicine, and specifically reproductive medicine, will enable clinicians to both create and utilize machine learning-based innovations for the furthering of reproductive medicine and ultimately achieving the goal of building of healthy families.

Recent advances of artificial intelligence in melanoma clinical practice

Skin melanoma is a lethal cancer. The incidence of melanoma is increasing rapidly in all regions of the world. Despite significant breakthroughs in melanoma treatment in recent years, precise diagnosis of melanoma is still a challenge in some cases. Even specialized physicians may need time and effort to make accurate judgments. As artificial intelligence (AI) technology advances into medical practice, it may bring new solutions to this problem based on its efficiency, accuracy, and speed. This paper summarizes the recent progress of AI in melanoma-related applications, including melanoma diagnosis and classification, the discovery of new medication, guiding treatment, and prognostic assessment. The paper also compares the effectiveness of various algorithms in melanoma application and suggests future research directions for AI in melanoma clinical practice.

CLASSICA: Validating artificial intelligence in classifying cancer in real time during surgery

AIM

Treatment pathways for significant rectal polyps differ depending on the underlying pathology, but pre-excision profiling is imperfect. It has been demonstrated that differences in fluorescence perfusion signals following injection of indocyanine green (ICG) can be analysed mathematically and, with the assistance of artificial intelligence (AI), used to classify tumours endoscopically as benign or malignant. This study aims to validate this method of characterization across multiple clinical sites regarding its generalizability, usability and accuracy while developing clinical-grade software to enable it to become a useful method.

METHODS

The CLASSICA study is a prospective, unblinded multicentre European observational study aimed to validate the use of AI analysis of ICG fluorescence for intra-operative tissue characterization. Six hundred patients undergoing transanal endoscopic evaluation of significant rectal polyps and tumours will be enrolled in at least five clinical sites across the European Union over a 4-year period. Video recordings will be analysed regarding dynamic fluorescence patterns centrally as software is developed to enable analysis with automatic classification to happen locally. AI-based classification and subsequently guided intervention will be compared with the current standard of care including biopsies, final specimen pathology and patient outcomes.

DISCUSSION

CLASSICA will validate the use of AI in the analysis of ICG fluorescence for the purposes of classifying significant rectal polyps and tumours endoscopically. Follow-on studies will compare AI-guided targeted biopsy or, indeed, AI characterization alone with traditional biopsy and AI-guided local excision versus traditional excision with regard to marginal clearance and recurrence.

Artificial intelligence in psoriasis: Where we are and where we are going

Artificial intelligence (AI) is a field of computer science that involves the development of programs designed to replicate human cognitive processes and the analysis of complex data. In dermatology, which is predominantly a visual-based diagnostic field, AI has become increasingly important in improving professional processes, particularly in the diagnosis of psoriasis. In this review, we summarized current AI applications in psoriasis: (i) diagnosis, including identification, classification, lesion segmentation, lesion severity and area scoring; (ii) treatment, including prediction treatment efficiency and prediction candidate drugs; (iii) management, including e-health and preventive medicine. Key challenges and future aspects of AI in psoriasis were also discussed, in hope of providing potential directions for future studies.

Knowledge and Perception of the Use of AI and its Implementation in the Field of Radiology: Cross-Sectional Study

BACKGROUND

Artificial Intelligence (AI) has been developing for decades, but in recent years its use in the field of health care has experienced an exponential increase. Currently, there is little doubt that these tools have transformed clinical practice. Therefore, it is important to know how the population perceives its implementation to be able to propose strategies for acceptance and implementation and to improve or prevent problems arising from future applications.

OBJECTIVE

This study aims to describe the population's perception and knowledge of the use of AI as a health support tool and its application to radiology through a validated questionnaire, in order to develop strategies aimed at increasing acceptance of AI use, reducing possible resistance to change and identifying possible sociodemographic factors related to perception and knowledge.

METHODS

A cross-sectional observational study was conducted using an anonymous and voluntarily validated questionnaire aimed at the entire population of Catalonia aged 18 years or older. The survey addresses 4 dimensions defined to describe users' perception of the use of AI in radiology, (1) "distrust and accountability," (2) "personal interaction," (3) "efficiency," and (4) "being informed," all with questions in a Likert scale format. Results closer to 5 refer to a negative perception of the use of AI, while results closer to 1 express a positive perception. Univariate and bivariate analyses were performed to assess possible associations between the 4 dimensions and sociodemographic characteristics.

RESULTS

A total of 379 users responded to the survey, with an average age of 43.9 (SD 17.52) years and 59.8% (n=226) of them identified as female. In addition, 89.8% (n=335) of respondents indicated that they understood the concept of AI. Of the 4 dimensions analyzed, "distrust and accountability" obtained a mean score of 3.37 (SD 0.53), "personal interaction" obtained a mean score of 4.37 (SD 0.60), "efficiency" obtained a mean score of 3.06 (SD 0.73) and "being informed" obtained a mean score of 3.67 (SD 0.57). In relation to the "distrust and accountability" dimension, women, people older than 65 years, the group with university studies, and the population that indicated not understanding the AI concept had significantly more distrust in the use of AI. On the dimension of "being informed," it was observed that the group with university studies rated access to information more positively and those who indicated not understanding the concept of AI rated it more negatively.

CONCLUSIONS

The majority of the sample investigated reported being familiar with the concept of AI, with varying degrees of acceptance of its implementation in radiology. It is clear that the most conflictive dimension is "personal interaction," whereas "efficiency" is where there is the greatest acceptance, being the dimension in which there are the best expectations for the implementation of AI in radiology.

Innovation in Actinic Keratosis Assessment: Artificial Intelligence-Based Approach to LC-OCT PRO Score Evaluation

Actinic keratosis (AK) is a common skin cancer in situ that can progress to invasive SCC. Line-field confocal optical coherence tomography (LC-OCT) has emerged as a non-invasive imaging technique that can aid in diagnosis. Recently, machine-learning algorithms have been developed that can automatically assess the PRO score of AKs based on the dermo-epidermal junction's (DEJ's) protrusion on LC-OCT images. A dataset of 19.898 LC-OCT images from 80 histologically confirmed AK lesions was used to test the performance of a previous validated artificial intelligence (AI)-based LC-OCT assessment algorithm. AI-based PRO score assessment was compared to the imaging experts' visual score. Additionally, undulation of the DEJ, the number of protrusions detected within the image, and the maximum depth of the protrusions were computed. Our results show that AI-automated PRO grading is highly comparable to the visual score, with an agreement of 71.3% for the lesions evaluated. Furthermore, this AI-based assessment was significantly faster than the regular visual PRO score assessment. The results confirm our previous findings of the pilot study in a larger cohort that the AI-based grading of LC-OCT images is a reliable and fast tool to optimize the efficiency of visual PRO score grading. This technology has the potential to improve the accuracy and speed of AK diagnosis and may lead to better clinical outcomes for patients.

Diagnosing Systemic Disorders with AI Algorithms Based on Ocular Images

The advent of artificial intelligence (AI), especially the state-of-the-art deep learning frameworks, has begun a silent revolution in all medical subfields, including ophthalmology. Due to their specific microvascular and neural structures, the eyes are anatomically associated with the rest of the body. Hence, ocular image-based AI technology may be a useful alternative or additional screening strategy for systemic diseases, especially where resources are scarce. This review summarizes the current applications of AI related to the prediction of systemic diseases from multimodal ocular images, including cardiovascular diseases, dementia, chronic kidney diseases, and anemia. Finally, we also discuss the current predicaments and future directions of these applications.

Automatic Segmentation of Psoriasis Skin Images Using Adaptive Chimp Optimization Algorithm-Based CNN

Psoriasis is a severe skin disease that is surveyed outwardly by dermatologists. In recent years, computer vision is the major solution for diagnosing the psoriasis skin disease by segmenting the infected skin images. Besides, many researchers had presented efficient machine learning techniques for segmenting the psoriasis skin images. Nevertheless, accuracy and time consumption of the model are further to be improved. Thus, in this work, we present adaptive chimp optimization algorithm (AChOA)-based convolutional neural network (CNN) which is introduced for automatic segmentation of psoriasis skin images. After pre-processing, the input images are segmented using AChOA-CNN model where weight and bias values of CNN are optimized with the AChOA. The search ability of ChOA is enhanced by adapting the chaotic sequence based on tent map. At final, from the segmented output images, artifacts are removed by applying the threshold module. From the simulation, we attain 97% of accuracy.

Analysis: Flawed Datasets of Monkeypox Skin Images

The self-proclaimed first publicly available dataset of Monkeypox skin images consists of medically irrelevant images extracted from Google and photography repositories through a process denominated web-scrapping. Yet, this did not stop other researchers from employing it to build Machine Learning (ML) solutions aimed at computer-aided diagnosis of Monkeypox and other viral infections presenting skin lesions. Neither did it stop the reviewers or editors from publishing these subsequent works in peer-reviewed journals. Several of these works claimed extraordinary performance in the classification of Monkeypox, Chickenpox and Measles, employing ML and the aforementioned dataset. In this work, we analyse the initiator work that has catalysed the development of several ML solutions, and whose popularity is continuing to grow. Further, we provide a rebuttal experiment that showcases the risks of such methodologies, proving that the ML solutions do not necessarily obtain their performance from the features relevant to the diseases at issue.

Recent Progress in the Diagnosis and Treatment of Melanoma and Other Skin Cancers

In this Special Issue, the reader will find nine papers regarding recent progress in diagnosis and treatment to optimize the clinical management of melanoma and non-melanoma skin cancer [...]

Exploring the potential of artificial intelligence in improving skin lesion diagnosis in primary care

Dermatological conditions are a relevant health problem. Machine learning (ML) models are increasingly being applied to dermatology as a diagnostic decision support tool using image analysis, especially for skin cancer detection and disease classification. The objective of this study was to perform a prospective validation of an image analysis ML model, which is capable of screening 44 skin diseases, comparing its diagnostic accuracy with that of General Practitioners (GPs) and teledermatology (TD) dermatologists in a real-life setting. Prospective, diagnostic accuracy study including 100 consecutive patients with a skin problem who visited a participating GP in central Catalonia, Spain, between June 2021 and October 2021. The skin issue was first assessed by the GPs. Then an anonymised skin disease picture was taken and uploaded to the ML application, which returned a list with the Top-5 possible diagnosis in order of probability. The same image was then sent to a dermatologist via TD for diagnosis, as per clinical practice. The GPs Top-3, ML model's Top-5 and dermatologist's Top-3 assessments were compared to calculate the accuracy, sensitivity, specificity and diagnostic accuracy of the ML models. The overall Top-1 accuracy of the ML model (39%) was lower than that of GPs (64%) and dermatologists (72%). When the analysis was limited to the diagnoses on which the algorithm had been explicitly trained (n = 82), the balanced Top-1 accuracy of the ML model increased (48%) and in the Top-3 (75%) was comparable to the GPs Top-3 accuracy (76%). The Top-5 accuracy of the ML model (89%) was comparable to the dermatologist Top-3 accuracy (90%). For the different diseases, the sensitivity of the model (Top-3 87% and Top-5 96%) is higher than that of the clinicians (Top-3 GPs 76% and Top-3 dermatologists 84%) only in the benign tumour pathology group, being on the other hand the most prevalent category (n = 53). About the satisfaction of professionals, 92% of the GPs considered it as a useful diagnostic support tool (DST) for the differential diagnosis and in 60% of the cases as an aid in the final diagnosis of the skin lesion. The overall diagnostic accuracy of the model in this study, under real-life conditions, is lower than that of both GPs and dermatologists. This result aligns with the findings of few existing prospective studies conducted under real-life conditions. The outcomes emphasize the significance of involving clinicians in the training of the model and the capability of ML models to assist GPs, particularly in differential diagnosis. Nevertheless, external testing in real-life conditions is crucial for data validation and regulation of these AI diagnostic models before they can be used in primary care.

Analyzing the Predictability of an Artificial Intelligence App (Tibot) in the Diagnosis of Dermatological Conditions: A Cross-sectional Study

BACKGROUND

Artificial intelligence (AI) aims to create programs that reproduce human cognition and processes involved in interpreting complex data. Dermatology relies on morphological features and is ideal for applying AI image recognition for assisted diagnosis. Tibot is an AI app that analyzes skin conditions and works on the principle of a convolutional neural network. Appropriate research analyzing the accuracy of such apps is necessary.

OBJECTIVE

This study aims to analyze the predictability of the Tibot AI app in the identification of dermatological diseases as compared to a dermatologist.

METHODS

This is a cross-sectional study. After taking informed consent, photographs of lesions of patients with different skin conditions were uploaded to the app. In every condition, the AI predicted three diagnoses based on probability, and these were compared with that by a dermatologist. The ability of the AI app to predict the actual diagnosis in the top one and top three anticipated diagnoses (prediction accuracy) was used to evaluate the app's effectiveness. Sensitivity, specificity, and positive predictive value were also used to assess the app's performance. Chi-square test was used to contrast categorical variables. P<.05 was considered statistically significant.

RESULTS

A total of 600 patients were included. Clinical conditions included alopecia, acne, eczema, immunological disorders, pigmentary disorders, psoriasis, infestation, tumors, and infections. In the anticipated top three diagnoses, the app's mean prediction accuracy was 96.1% (95% CI 94.3%-97.5%), while for the exact diagnosis, it was 80.6% (95% CI 77.2%-83.7%). The prediction accuracy (top one) for alopecia, acne, pigmentary disorders, and fungal infections was 97.7%, 91.7%, 88.5%, and 82.9%, respectively. Prediction accuracy (top three) for alopecia, eczema, and tumors was 100%. The sensitivity and specificity of the app were 97% (95% CI 95%-98%) and 98% (95% CI 98%-99%), respectively. There is a statistically significant association between clinical and AI-predicted diagnoses in all conditions (P<.001).

CONCLUSIONS

The AI app has shown promising results in diagnosing various dermatological conditions, and there is great potential for practical applicability.

Through the eyes into the brain, using artificial intelligence

Introduction: Detection of neurological conditions is of high importance in the current context of increasingly ageing populations. Imaging of the retina and the optic nerve head represents a unique opportunity to detect brain diseases, but requires specific human expertise. We review the current outcomes of artificial intelligence (AI) methods applied to retinal imaging for the detection of neurological and neuro-ophthalmic conditions. Method: Current and emerging concepts related to the detection of neurological conditions, using AI-based investigations of the retina in patients with brain disease were examined and summarised. Results: Papilloedema due to intracranial hypertension can be accurately identified with deep learning on standard retinal imaging at a human expert level. Emerging studies suggest that patients with Alzheimer’s disease can be discriminated from cognitively normal individuals, using AI applied to retinal images. Conclusion: Recent AI-based systems dedicated to scalable retinal imaging have opened new perspectives for the detection of brain conditions directly or indirectly affecting retinal structures. However, further validation and implementation studies are required to better understand their potential value in clinical practice. Keywords: Alzheimer’s disease, deep learning, dementia, optic neuropathy, papilloedema

Therapeutic application of machine learning in psoriasis: A Prisma systematic review

Dermatology, being a predominantly visual-based diagnostic field, has found itself to be at the epitome of artificial intelligence (AI)-based advances. Machine learning (ML), a subset of AI, goes a step further by recognizing patterns from data and teaches machines to automatically learn tasks. Although artificial intelligence in dermatology is mostly developed in melanoma and skin cancer diagnosis, advances in AI and ML have gone far ahead and found its application in ulcer assessment, psoriasis, atopic dermatitis, onychomycosis, etc. This article is focused on the application of ML in the therapeutic aspect of psoriasis.

Artificial Intelligence in microbiomes analysis: A review of applications in dermatology

Microorganisms are closely related to skin diseases, and microbiological imbalances or invasions of exogenous pathogens can be a source of various skin diseases. The development and prognosis of such skin diseases are also closely related to the type and composition ratio of microorganisms present. Therefore, through detection of the characteristics and changes in microorganisms, the possibility for diagnosis and prediction of skin diseases can be markedly improved. The abundance of microorganisms and an understanding of the vast amount of biological information associated with these microorganisms has been a formidable task. However, with advances in large-scale sequencing, artificial intelligence (AI)-related machine learning can serve as a means to analyze large-scales of data related to microorganisms along with determinations regarding the type and status of diseases. In this review, we describe some uses of this exciting, new emerging field. In specific, we described the recognition of fungi with convolutional neural networks (CNN), the combined application of microbial genome sequencing and machine learning and applications of AI in the diagnosis of skin diseases as related to the gut-skin axis.

Conventional and three-dimensional photography as a tool to map distribution patterns of in-transit melanoma metastases on the lower extremity

Background

In melanoma, in-transit metastases characteristically occur at the lower extremity along lymphatic vessels.

Objectives

The objective of this study was to evaluate conventional or three-dimensional photography as a tool to analyze in-transit metastasis pattern of melanoma of the lower extremity. In addition, we assessed risk factors for the development of in-transit metastases in cutaneous melanoma.

Methods

In this retrospective, monocentric study first we compared the clinical data of all evaluable patients with in-transit metastases of melanoma on the lower extremity (n = 94) with melanoma patients without recurrence of disease (n = 288). In addition, based on conventional (n = 24) and three-dimensional photography (n = 22), we defined the specific distribution patterns of the in-transit metastases on the lower extremity.

Results

Using a multivariate analysis we identified nodular melanoma, tumor thickness, and ulceration as independent risk factors to develop in-transit metastases ITM (n = 94). In patients with melanoma on the lower leg (n = 31), in-transit metastases preferentially developed along anatomically predefined lymphatic pathways. In contrast when analyzing in-transit metastases of melanoma on the foot (n = 15) no clear pattern could be visualized. In addition, no difference in distance between in-transit metastases and primary melanoma on the foot compared to the lower leg was observed using three-dimensional photography (n = 22).

Conclusion

A risk-adapted follow-up of melanoma patients to detect in-transit metastases can be applied by knowledge of the specific lymphatic drainage of the lower extremity. Our current analysis suggests a more complex lymphatic drainage of the foot.

Strategies to improve the diagnosis and clinical treatment of dermatophyte infections

INTRODUCTION

Significant problems are associated with the diagnosis and treatment of dermatophyte infections, which constitute the most common fungal infections of the skin. Although this is a common problem in the community, there are no adequate guidelines for the management of all forms of dermatophyte infections. Even if dermatophytes are correctly diagnosed, they sometimes exhibit poor susceptibility to several antifungal compounds. Therefore, long-term treatment may be needed, especially in immunosuppressed patients, for whom antifungal pharmacotherapy may be inconvenient owing to allergies and undesirable drug interaction-related effects.

AREAS COVERED

In this review article, problems related to the diagnosis and treatment of dermatophyte infections have been discussed, and suggestions to resolve these problems have been presented.

EXPERT OPINION

Pretreatment microscopic or mycological examinations should be performed for dermatophyte infections. In treatment-refractory cases, antifungal-resistant strains should be determined using antifungal susceptibility testing or via molecular methods. Natural herbal, laser, and photodynamic treatments can be used as alternative treatments in patients who cannot tolerate topical and systemic antifungal treatments.

Knowledge and perception of primary care healthcare professionals on the use of artificial intelligence as a healthcare tool

Objective

The rapid digitisation of healthcare data and the sheer volume being generated means that artificial intelligence (AI) is becoming a new reality in the practice of medicine. For this reason, describing the perception of primary care (PC) healthcare professionals on the use of AI as a healthcare tool and its impact in radiology is crucial to ensure its successful implementation.

Methods

Observational cross-sectional study, using the validated Shinners Artificial Intelligence Perception survey, aimed at all PC medical and nursing professionals in the health region of Central Catalonia.

Results

The survey was sent to 1068 health professionals, of whom 301 responded. And 85.7% indicated that they understood the concept of AI but there were discrepancies in the use of this tool; 65.8% indicated that they had not received any AI training and 91.4% that they would like to receive training. The mean score for the professional impact of AI was 3.62 points out of 5 (standard deviation (SD)  =  0.72), with a higher score among practitioners who had some prior knowledge of and interest in AI. The mean score for preparedness for AI was 2.76 points out of 5 (SD  =  0.70), with higher scores for nursing and those who use or do not know if they use AI.

Conclusions

The results of this study show that the majority of professionals understood the concept of AI, perceived its impact positively, and felt prepared for its implementation. In addition, despite being limited to a diagnostic aid, the implementation of AI in radiology was a high priority for these professionals.

Artificial Intelligence in Dermatology: Challenges and Perspectives

Artificial intelligence (AI) based on machine learning and convolutional neuron networks (CNN) is rapidly becoming a realistic prospect in dermatology. Non-melanoma skin cancer is the most common cancer worldwide and melanoma is one of the deadliest forms of cancer. Dermoscopy has improved physicians' diagnostic accuracy for skin cancer recognition but unfortunately it remains comparatively low. AI could provide invaluable aid in the early evaluation and diagnosis of skin cancer. In the last decade, there has been a breakthrough in new research and publications in the field of AI. Studies have shown that CNN algorithms can classify skin lesions from dermoscopic images with superior or at least equivalent performance compared to clinicians. Even though AI algorithms have shown very promising results for the diagnosis of skin cancer in reader studies, their generalizability and applicability in everyday clinical practice remain elusive. Herein we attempted to summarize the potential pitfalls and challenges of AI that were underlined in reader studies and pinpoint strategies to overcome limitations in future studies. Finally, we tried to analyze the advantages and opportunities that lay ahead for a better future for dermatology and patients, with the potential use of AI in our practices.

Artificial Intelligence in Dermatology Image Analysis: Current Developments and Future Trends

BACKGROUND

Thanks to the rapid development of computer-based systems and deep-learning-based algorithms, artificial intelligence (AI) has long been integrated into the healthcare field. AI is also particularly helpful in image recognition, surgical assistance and basic research. Due to the unique nature of dermatology, AI-aided dermatological diagnosis based on image recognition has become a modern focus and future trend. Key scientific concepts of review: The use of 3D imaging systems allows clinicians to screen and label skin pigmented lesions and distributed disorders, which can provide an objective assessment and image documentation of lesion sites. Dermatoscopes combined with intelligent software help the dermatologist to easily correlate each close-up image with the corresponding marked lesion in the 3D body map. In addition, AI in the field of prosthetics can assist in the rehabilitation of patients and help to restore limb function after amputation in patients with skin tumors.

THE AIM OF THE STUDY

For the benefit of patients, dermatologists have an obligation to explore the opportunities, risks and limitations of AI applications. This study focuses on the application of emerging AI in dermatology to aid clinical diagnosis and treatment, analyzes the current state of the field and summarizes its future trends and prospects so as to help dermatologists realize the impact of new technological innovations on traditional practices so that they can embrace and use AI-based medical approaches more quickly.

Improvement of Mucosal Lesion Diagnosis with Machine Learning Based on Medical and Semiological Data: An Observational Study

Despite artificial intelligence used in skin dermatology diagnosis is booming, application in oral pathology remains to be developed. Early diagnosis and therefore early management, remain key points in the successful management of oral mucosa cancers. The objective was to develop and evaluate a machine learning algorithm that allows the prediction of oral mucosa lesions diagnosis. This cohort study included patients followed between January 2015 and December 2020 in the oral mucosal pathology consultation of the Toulouse University Hospital. Photographs and demographic and medical data were collected from each patient to constitute clinical cases. A machine learning model was then developed and optimized and compared to 5 models classically used in the field. A total of 299 patients representing 1242 records of oral mucosa lesions were used to train and evaluate machine learning models. Our model reached a mean accuracy of 0.84 for diagnostic prediction. The specificity and sensitivity range from 0.89 to 1.00 and 0.72 to 0.92, respectively. The other models were proven to be less efficient in performing this task. These results suggest the utility of machine learning-based tools in diagnosing oral mucosal lesions with high accuracy. Moreover, the results of this study confirm that the consideration of clinical data and medical history, in addition to the lesion itself, appears to play an important role.

Artificial Intelligence Confirming Treatment Success: The Role of Gender- and Age-Specific Scales in Performance Evaluation

In plastic surgery and cosmetic dermatology, photographic data are an invaluable element of research and clinical practice. Additionally, the use of before and after images is a standard documentation method for procedures, and these images are particularly useful in consultations for effective communication with the patient. An artificial intelligence (AI)-based approach has been proven to have significant results in medical dermatology, plastic surgery, and antiaging procedures in recent years, with applications ranging from skin cancer screening to 3D face reconstructions, the prediction of biological age and perceived age. The increasing use of AI and computer vision methods is due to their noninvasive nature and their potential to provide remote diagnostics. This is especially helpful in instances where traveling to a physical office is complicated, as we have experienced in recent years with the global coronavirus pandemic. However, one question remains: how should the results of AI-based analysis be presented to enable personalization? In this paper, the author investigates the benefit of using gender- and age-specific scales to present skin parameter scores calculated using AI-based systems when analyzing image data.

Neural Networks-Based On-Site Dermatologic Diagnosis through Hyperspectral Epidermal Images

Cancer originates from the uncontrolled growth of healthy cells into a mass. Chromophores, such as hemoglobin and melanin, characterize skin spectral properties, allowing the classification of lesions into different etiologies. Hyperspectral imaging systems gather skin-reflected and transmitted light into several wavelength ranges of the electromagnetic spectrum, enabling potential skin-lesion differentiation through machine learning algorithms. Challenged by data availability and tiny inter and intra-tumoral variability, here we introduce a pipeline based on deep neural networks to diagnose hyperspectral skin cancer images, targeting a handheld device equipped with a low-power graphical processing unit for routine clinical testing. Enhanced by data augmentation, transfer learning, and hyperparameter tuning, the proposed architectures aim to meet and improve the well-known dermatologist-level detection performances concerning both benign-malignant and multiclass classification tasks, being able to diagnose hyperspectral data considering real-time constraints. Experiments show 87% sensitivity and 88% specificity for benign-malignant classification and specificity above 80% for the multiclass scenario. AUC measurements suggest classification performance improvement above 90% with adequate thresholding. Concerning binary segmentation, we measured skin DICE and IOU higher than 90%. We estimated 1.21 s, at most, consuming 5 Watts to segment the epidermal lesions with the U-Net++ architecture, meeting the imposed time limit. Hence, we can diagnose hyperspectral epidermal data assuming real-time constraints.

Histologic Screening of Malignant Melanoma, Spitz, Dermal and Junctional Melanocytic Nevi Using a Deep Learning Model

OBJECTIVE

The integration of an artificial intelligence tool into pathologists' workflow may lead to a more accurate and timely diagnosis of melanocytic lesions, directly patient care. The objective of this study was to create and evaluate the performance of such a model in achieving clinical-grade diagnoses of Spitz nevi, dermal and junctional melanocytic nevi, and melanomas.

METHODS

We created a beginner-level training environment by teaching our algorithm to perform cytologic inferences on 136,216 manually annotated tiles of hematoxylin and eosin-stained slides consisting of unequivocal melanocytic nevi, Spitz nevi, and invasive melanoma cases. We sequentially trained and tested our network to provide a final diagnosis-classification on 39 cases in total. Positive predictive value (precision) and sensitivity (recall) were used to measure our performance.

RESULTS

The tile-classification algorithm predicted the 136,216 irrelevant, melanoma, melanocytic nevi, and Spitz nevi tiles at sensitivities of 96%, 93%, 94% and 73%, respectively. The final trained model was able to correctly classify and predict the correct diagnosis in 85.7% of unseen cases (n = 28), reporting at or near screening-level performances for precision and recall of melanoma (76.2%, 100.0%), melanocytic nevi (100.0%, 75.0%), and Spitz nevi (100.0%, 75.0%).

CONCLUSIONS

Our pilot study proves that convolutional networks trained on cellular morphology to classify melanocytic proliferations can be used as a powerful tool to assist pathologists in screening for melanoma versus other benign lesions.

Deep Convolutional Neural Networks for Onychomycosis Detection using Microscopic Images with KOH Examination

BACKGROUND

The diagnosis of superficial fungal infections is still mostly based on direct microscopic examination with Potassium Hydroxide solution. However, this method can be time consuming and its diagnostic accuracy rates vary widely depending on the clinician's experience.

OBJECTIVES

This study presents a deep neural network structure that enables the rapid solutions for these problems and can perform automatic fungi detection in grayscale images without dyes.

METHODS

160 microscopic full field photographs containing the fungal element, obtained from patients with onychomycosis, and 297 microscopic full field photographs containing dissolved keratin obtained from normal nails were collected. Smaller patches containing fungi (n=1835) and keratin (n=5238) were extracted from these full field images. In order to detect fungus and keratin, VGG16 and InceptionV3 models were developed by the use of these patches. The diagnostic performance of models was compared with 16 dermatologists by using 200 test patches.

RESULTS

For the VGG16 model, the InceptionV3 model and 16 dermatologists; mean accuracy rates were 88.10%±0.8%, 88.78%±0.35%, and 74.53%±8.57%, respectively; mean sensitivity rates were 75.04%±2.73%, 74.93%±4.52%, and 74.81%±19.51%, respectively; and mean specificity rates were 92.67%±1.17%, 93.78%±1.74%, and 74.25%±18.03%, respectively. The models were statistically superior to dermatologists according to rates of accuracy and specificity but not to sensitivity (p < 0.0001, p < 0.005, and p > 0.05, respectively). Area under curve values of the VGG16 and InceptionV3 models were 0.9339 and 0.9292, respectively.

CONCLUSION

Our research demonstrates that it is possible to build an automated system capable of detecting fungi present in microscopic images employing the proposed deep learning models. It has great potential for fungal detection applications based on AI.

Emerging High-Frequency Ultrasound Imaging in Medical Cosmetology

Cosmetic skin diseases are a part of many dermatological concerns brought up by patients, which negatively affect mental health and quality of life. Imaging technology has an established role in the diagnosis of cosmetic skin diseases by recognizing information on deep skin lesions. Due to the complex physiological and pathological nature of cosmetic skin diseases, the diagnostic imaging performance varies greatly. Developing noninvasive technology models with wide applicability, particularly high-frequency ultrasound (HFUS), which is able to achieve high-resolution imaging of the skin from the stratum corneum down to the deep fascia, is of great significance to medical cosmetology. To explore the great potential of HFUS in cosmetic skin diseases, a narrative review of literature from PubMed and Web of Science published between 1985 and 2022 was conducted. This narrative review focuses on the progression of HFUS imaging in medical cosmetology, especially on its promising application in the quantitative evaluation and differential diagnosis of cutaneous pathological scar, port wine stain (PWS), acne, skin aging, and other cosmetic applications.

An Audit and Survey of Informal Use of Instant Messaging for Dermatology in District Hospitals in KwaZulu-Natal, South Africa

Background. In KwaZulu-Natal (KZ-N), South Africa, recent reports have indicated that spontaneous use of smartphones has occurred, providing access to specialist dermatological care to remote areas. This informal use has raised a number of practical, legal, regulatory, and ethical concerns. Aim. To assess the nature and content of WhatsApp messages sent to dermatologists, to determine the referring doctors’ reasons for, and satisfaction with, their interactions, as well as their knowledge of legal, regulatory, and ethical requirements. Methods. A retrospective study of WhatsApp messages between referring doctors and dermatologists, as well as a cross-sectional survey of doctors working at district hospitals in KZ-N who used IM for teledermatology. Results. Use of IM (primarily WhatsApp) for teledermatology was almost universal, but often not considered ‘telemedicine’. Few referring doctors were aware of South Africa’s ethical guidelines and their requirements, and few of those who did followed them, e.g., the stipulated and onerous consent process and existing privacy and security legislations. No secure methods for record keeping or data storage of WhatsApp content were used. A desire to formalize the service existed. Conclusions. Based upon these findings, it was proposed that a number of described steps be followed in order to formalize the use of IM for teledermatology.

Diagnosing Onychomycosis: What's New?

An overview of the long-established methods of diagnosing onychomycosis (potassium hydroxide testing, fungal culture, and histopathological examination) is provided followed by an outline of other diagnostic methods currently in use or under development. These methods generally use one of two diagnostic techniques: visual identification of infection (fungal elements or onychomycosis signs) or organism identification (typing of fungal genus/species). Visual diagnosis (dermoscopy, optical coherence tomography, confocal microscopy, UV fluorescence excitation) provides clinical evidence of infection, but may be limited by lack of organism information when treatment decisions are needed. The organism identification methods (lateral flow techniques, polymerase chain reaction, MALDI-TOF mass spectroscopy and Raman spectroscopy) seek to provide faster and more reliable identification than standard fungal culture methods. Additionally, artificial intelligence methods are being applied to assist with visual identification, with good success. Despite being considered the 'gold standard' for diagnosis, clinicians are generally well aware that the established methods have many limitations for diagnosis. The new techniques seek to augment established methods, but also have advantages and disadvantages relative to their diagnostic use. It remains to be seen which of the newer methods will become more widely used for diagnosis of onychomycosis. Clinicians need to be aware of the limitations of diagnostic utility calculations as well, and look beyond the numbers to assess which techniques will provide the best options for patient assessment and management.