Deep learning-based diagnosis models for onychomycosis in dermoscopy

Artificial intelligence in assisting pathogenic microorganism diagnosis and treatment: a review of infectious skin diseases

The skin, the largest organ of the human body, covers the body surface and serves as a crucial barrier for maintaining internal environmental stability. Various microorganisms such as bacteria, fungi, and viruses reside on the skin surface, and densely arranged keratinocytes exhibit inhibitory effects on pathogenic microorganisms. The skin is an essential barrier against pathogenic microbial infections, many of which manifest as skin lesions. Therefore, the rapid diagnosis of related skin lesions is of utmost importance for early treatment and intervention of infectious diseases. With the continuous rapid development of artificial intelligence, significant progress has been made in healthcare, transforming healthcare services, disease diagnosis, and management, including a significant impact in the field of dermatology. In this review, we provide a detailed overview of the application of artificial intelligence in skin and sexually transmitted diseases caused by pathogenic microorganisms, including auxiliary diagnosis, treatment decisions, and analysis and prediction of epidemiological characteristics.

Advances in image-based diagnosis of nail disorders

This paper provides a comprehensive overview of image-based techniques, particularly focusing on their applications and advancements in the context of nail disorders. Nowadays, high-resolution digital cameras and dermoscopes enable dermatologists to capture detailed images of nail abnormalities, facilitating early diagnosis and meticulous tracking of disease progression. Onychoscopy is now a routine technique with well-known criteria for the diagnosis, but recent developments allow us to visualize certain diseases better. Imaging modalities like high-frequency ultrasound, magnetic resonance imaging, optical coherence tomography and confocal microscopy are being increasingly adopted for their superior diagnostic capabilities. These techniques are described in their technology, scanning protocols, normal findings, advantages and limitations. Moreover, the integration of technology in patient education has fostered a more informed patient population, capable of actively participating in their disease monitoring and treatment regimens. Proper training, validation, regulation and ethical considerations are, however, essential when integrating technology into healthcare practices. Imaging technologies that present the potential to add critical anatomical information to clinical diagnoses within reasonable costs and are available worldwide are the ones that will probably be used the most.

Efficacy of Amorolfine in Onychomycosis Treatment: A Mixed-Effects Models and Multivariate Logistic Regression Analysis

BACKGROUND

Onychomycosis (OM) is a common nail infection treated with amorolfine hydrochloride nail lacquer in China. Monitoring drug concentrations and using dermoscopy to evaluate treatment efficacy may provide new insights.

OBJECTIVE

The study aims to analyse amorolfine concentrations in nails with mild to moderate OM, assess treatment outcomes using dermoscopy and explore factors influencing drug concentrations and efficacy.

METHODS

Patients with mild to moderate OM confirmed by fungal microscopy were enrolled. Amorolfine nail lacquer was applied twice weekly for 36 weeks. Monthly nail samples measured amorolfine concentrations using liquid chromatography. Dermoscopy was performed before and after treatment to evaluate responses. Mixed-effects models and logistic regression analysed factors affecting drug concentrations and outcomes.

RESULTS

Ninety-seven nails were included. Amorolfine concentrations increased over time, with higher levels in females, fingernails, 2nd-5th digits and superficial white OM (p < 0.05). Age was a risk factor, while drug concentration and OM type were protective for clinical efficacy (p < 0.05). Peak concentration correlated with clinical (r = 0.487, p = 0.000) and mycological (r = 0.433, p = 0.000) responses. Dermoscopic features improved significantly in successful cases (p < 0.05).

LIMITATIONS

In the assessment of fungal efficacy, only fungal microscopy was used, and fungal cultures were not performed. The study was limited by a small sample size and the lack of a longer follow-up to assess relapse.

CONCLUSION

Amorolfine concentrations vary with patient and nail characteristics, influencing efficacy. Dermoscopy is valuable for monitoring OM treatment.

A review of psoriasis image analysis based on machine learning

Machine Learning (ML), an Artificial Intelligence (AI) technique that includes both Traditional Machine Learning (TML) and Deep Learning (DL), aims to teach machines to automatically learn tasks by inferring patterns from data. It holds significant promise in aiding medical care and has become increasingly important in improving professional processes, particularly in the diagnosis of psoriasis. This paper presents the findings of a systematic literature review focusing on the research and application of ML in psoriasis analysis over the past decade. We summarized 53 publications by searching the Web of Science, PubMed and IEEE Xplore databases and classified them into three categories: (i) lesion localization and segmentation; (ii) lesion recognition; (iii) lesion severity and area scoring. We have presented the most common models and datasets for psoriasis analysis, discussed the key challenges, and explored future trends in ML within this field. Our aim is to suggest directions for subsequent research.

Artificial Intelligence in the Diagnosis of Onychomycosis-Literature Review

Onychomycosis is a common fungal nail infection that is difficult to diagnose due to its similarity to other nail conditions. Accurate identification is essential for effective treatment. The current gold standard methods include microscopic examination with potassium hydroxide, fungal cultures, and Periodic acid-Schiff biopsy staining. These conventional techniques, however, suffer from high turnover times, variable sensitivity, reliance on human interpretation, and costs. This study examines the potential of integrating AI (artificial intelligence) with visualization tools like dermoscopy and microscopy to improve the accuracy and efficiency of onychomycosis diagnosis. AI algorithms can further improve the interpretation of these images. The review includes 14 studies from PubMed and IEEE databases published between 2010 and 2024, involving clinical and dermoscopic pictures, histopathology slides, and KOH microscopic images. Data extracted include study type, sample size, image assessment model, AI algorithms, test performance, and comparison with clinical diagnostics. Most studies show that AI models achieve an accuracy comparable to or better than clinicians, suggesting a promising role for AI in diagnosing onychomycosis. Nevertheless, the niche nature of the topic indicates a need for further research.

A comprehensive review of nondermatophyte mould onychomycosis: Epidemiology, diagnosis and management

Nondermatophyte moulds (NDMs) are widely distributed and can be detected in association with mycotic nails; however, sometimes it can be challenging to establish the role of NDMs in the pathogenesis of onychomycosis (i.e. causative vs. contaminant). In studies where the ongoing invasive presence of NDMs is confirmed through repeat cultures, the global prevalence of NDMs in onychomycosis patients is estimated at 6.9% with the 3 most common genus being: Aspergillus, Scopulariopsis and Fusarium. NDM onychomycosis can, in many cases, appear clinically indistinguishable from dermatophyte onychomycosis. Clinical features suggestive of NDMs include proximal subungual onychomycosis with paronychia associated with Aspergillus spp., Fusarium spp. and Scopulariopsis brevicaulis, as well as superficial white onychomycosis in a deep and diffused pattern associated with Aspergillus and Fusarium. Longitudinal streaks seen in patients with distal and lateral onychomycosis may serve as an additional indicator. For diagnosis, light microscopic examination should demonstrate fungal filaments consistent with an NDM with at least two independent isolations in the absence of a dermatophyte; the advent of molecular testing combined with histological assessment may serve as an alternative with improved sensitivity and turnover time. In most instances, antifungal susceptibility testing has limited value. Information on effective treatments for NDM onychomycosis is relatively scarce, unlike the situation in the study of dermatophyte onychomycosis. Terbinafine and itraconazole therapy (continuous and pulsed) appear effective to varying extents for treating onychomycosis caused by Aspergillus, Fusarium or Scopulariopsis. There is scant literature on oral treatments for Neoscytalidium.

Clinical value of dermoscopy in psoriasis

BACKGROUND

Dermoscopy is a noninvasive technique that has attracted increasing attention in the field of inflammatory skin diseases (such as psoriasis) in recent years.

OBJECTIVE

This study aimed to provide an up-to-date overview of the role of dermoscopy in the diagnosis and extra-diagnosis of psoriasis.

METHODS

This study sought to review the published literature regarding use of dermoscopy in the evaluation of psoriasis.

RESULTS

The diagnostic value of dermoscopy in psoriasis vulgaris, nail psoriasis, and other types of psoriasis was summarized from the aspects of vascular pattern, scale pattern, and other features. Meanwhile, the application value of dermoscopy in the differential diagnosis, efficacy and severity assessment, prediction and monitoring of psoriasis was discussed.

CONCLUSION

Dermoscopy has good clinical value in the diagnosis and differential diagnosis of psoriasis and shows great prospects for severity assessment and efficacy prediction monitoring.

Dermoscopic Features of Cutaneous Vasculitis

INTRODUCTION

Dermoscopy has become widespread in the diagnosis of inflammatory skin diseases. Cutaneous vasculitis (CV) is characterized by inflammation of vessels, and a rapid and reliable technique is required for the diagnosis.

OBJECTIVES

We aimed to define CV dermoscopic features and increase the diagnostic accuracy of dermoscopy with machine learning (ML) methods.

METHODS

Eighty-nine patients with clinically suspected CV were included in the study. Dermoscopic images were obtained before biopsy using a polarized dermoscopy. Dermoscopic images were independently evaluated, and interobserver variability was calculated. Decision Tree, Random Forest, and K-Nearest Neighbors were used as ML classification models.

RESULTS

The histopathological diagnosis of 58 patients was CV. Three patterns were observed: homogeneous pattern, mottled pattern, and meshy pattern. There was a significant difference in background color between the CV and non-CV groups (P = 0.001). The milky red and livedoid background color were specific markers in the differential diagnosis of CV (sensitivity 56.7%, specificity 96.3%, sensitivity 29.4%, specificity 99.2%, respectively). Red blotches were significantly more common in CV lesions (P = 0.038). Red dots, comma vessels, and scales were more common in the non-CV group (P = 0.002, P = 0.002, P = 0.003, respectively). Interobserver agreement was very good for both pattern (κ = 0.869) and background color analysis (κ = 0.846) (P < 0.001). According to ML classifiers, the background color and lack of scales were the most significant dermoscopic aspects of CV.

CONCLUSIONS

Dermoscopy may guide as a rapid and reliable technique in CV diagnosis. High accuracy rates obtained with ML methods may increase the success of dermoscopy.

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.

Updated Perspectives on the Diagnosis and Management of Onychomycosis

Onychomycosis is the most common nail disease encountered in clinical practice and can cause pain, difficulty with ambulation, and psycho-social problems. A thorough history and physical examination, including dermoscopy, should be performed for each patient presenting with nail findings suggestive of onychomycosis. Several approaches are available for definitive diagnostic testing, including potassium hydroxide and microscopy, fungal culture, histopathology, polymerase chain reaction, or a combination of techniques. Confirmatory testing should be performed for each patient prior to initiating any antifungal therapies. There are several different therapeutic options available, including oral and topical medications as well as device-based treatments. Oral antifungals are generally recommended for moderate to severe onychomycosis and have higher cure rates, while topical antifungals are recommended for mild to moderate disease and have more favorable safety profiles. Oral terbinafine, itraconazole, and griseofulvin and topical ciclopirox 8% nail lacquer, efinaconazole 10% solution, and tavaborole 5% solution are approved by the Food and Drug Administration for treatment of onychomycosis in the United States and amorolfine 5% nail lacquer is approved in Europe. Laser treatment is approved in the United States for temporary increases in clear nail, but clinical results are suboptimal. Oral fluconazole is not approved in the United States for onychomycosis treatment, but is frequently used off-label with good efficacy. Several novel oral, topical, and over-the-counter therapies are currently under investigation. Physicians should consider the disease severity, infecting pathogen, medication safety, efficacy and cost, and patient age, comorbidities, medication history, and likelihood of compliance when determining management plans. Onychomycosis is a chronic disease with high recurrence rates and patients should be counseled on an appropriate plan to minimize recurrence risk following effective antifungal therapy.

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.

The Spectrum of Spitz Melanocytic Lesions: From Morphologic Diagnosis to Molecular Classification

Spitz tumors represent a distinct subtype of melanocytic lesions with characteristic histopathologic features, some of which are overlapping with melanoma. More common in the pediatric and younger population, they can be clinically suspected by recognizing specific patterns on dermatoscopic examination, and several subtypes have been described. We now classify these lesions into benign Spitz nevi, intermediate lesions identified as “atypical Spitz tumors” (or Spitz melanocytoma) and malignant Spitz melanoma. More recently a large body of work has uncovered the molecular underpinning of Spitz tumors, including mutations in the HRAS gene and several gene fusions involving several protein kinases. Here we present an overarching view of our current knowledge and understanding of Spitz tumors, detailing clinical, histopathological and molecular features characteristic of these lesions.