New diagnostic and imaging technologies in dermatology

Jartarkar, Shishiria R; Patil, Anant; Wollina, Uwe; Gold, Michael H.; Stege, Henner; Grabbe, Stephan; Goldust, Mohamad

doi:10.1111/jocd.14499

Cited by 25 publications

(7 citation statements)

References 47 publications

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“…High‐frequency ultrasound has great penetration depth (8 mm) but poor axial (50 μm) and lateral resolution (350 μm). It has a limited diagnostic utility but is used to identify invasive skin cancer margins and nodal metastasis 25 . Compared to these two modalities, OCT has midrange capabilities.…”

Section: Discussionmentioning

confidence: 99%

Transforming the treatment of psoriasis to the 21st century: Detecting subclinical therapeutic response to secukinumab using optical coherence tomography as a prognostic indicator

et al. 2022

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Background: Optical coherence tomography (OCT) is a noninvasive imaging device that scans the skin up to 2 mm in depth. OCT can capture real-time epidermal thickness (ET) measurements and detect subclinical changes in inflammatory skin diseases like eczema and psoriasis. © 2022 Wiley Periodicals LLC. Objective: To determine if measuring ET with OCT can detect a subclinical therapeutic response in psoriasis treated with the biological therapy, secukinumab (an IL-17A antagonist). Design: Phase IV, single-center, open-label, and single-arm study. Participants: Twenty-six consecutive patients with moderate to severe plaque psoriasis. Measurements: Clinical, dermoscopic, and OCT images were obtained at each visit. The clinician measured disease severity with the Investigator's Global Assessment (IGA) and Psoriasis Area And Severity Index (PASI). OCT was used to scan the ET at the center of lesional skin (ET-L), along the border, and normal skin (ET-N) on the same body plane; their difference was noted as ΔET. Results: Initially, ET-L was greater than ET-N ( p < 0.0001), their differences decreased throughout the study, and there were no significant differences at Week 16 ( p = 0.48). Twenty-four (92%) patients achieved a 50% reduction in PASI score (PASI50); they had lower ΔET at Weeks 0, 1, 3, 4, and 8 compared to those who did not clear ( p < 0.04). Having a lower ΔET at Week 4 was associated with a shorter time to reach PASI50 ( p = 0.02). Conclusion: ET measurements using OCT can detect an early subclinical response to secukinumab compared to clinical scoring and identify nonresponders as early as 4 weeks.

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Section: Discussionmentioning

confidence: 99%

Transforming the treatment of psoriasis to the 21st century: Detecting subclinical therapeutic response to secukinumab using optical coherence tomography as a prognostic indicator

et al. 2022

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“…Even a minor distribution in the skin structure can alter the cutaneous cell organisation and lead to disease. This is why histology has been attributed as the main tool in diagnosing skin diseases 14 . Therefore, the acquisition of location specific information of gene expression has a very high impact in cutaneous research.…”

Section: Introductionmentioning

confidence: 99%

“…This is why histology has been attributed as the main tool in diagnosing skin diseases. 14 Therefore, the acquisition of location specific information of gene expression has a very high impact in cutaneous research.…”

mentioning

confidence: 99%

Spatial transcriptomics in human skin research

Tekkela

Theocharidis

McGrath

et al. 2023

Experimental Dermatology

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Tissues and organs of multicellular organisms are spatially organised by the compartmentalisation of cell types and subpopulations that are coordinated in internal gene regulatory networks, and by signalling from their surrounding tissue environment. In order to perform a subset of biologic processes, these cells differ in terms of localisation, morphology and gene expression patterns. 1 The development of single-cell methods has enabled studies that can retain cellular properties and heterogeneity at a single-cell resolution, which has led to a more detailed understanding of physiologic and disease conditions. 2 One of the most popular methods, single-cell RNA-sequencing (scRNA-seq), has made it possible to profile the whole transcriptome of each individual cell. 3,4 However, it results in the loss of information on spatial relationships among the cell populations since it entails dismantling of the original tissue into single-cell suspensions through mechanical and enzymatic dissociation steps, which could perturb or alter the cell's gene expression. 5 Recently, various techniques have been developed aiming for transcriptome mapping while preserving the spatial location of the expressed transcripts within intact tissues. 6 These methods, collectively known as spatial transcriptomics (ST), have been voted as the method of the year 2020 by Nature Methods and expect to provide remarkable novel insights in the

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“…The majority of diagnoses in dermatology are made primarily on visual pattern recognition and mainly depend on morphological traits. Its comprehensive clinical, dermatoscopic, and dermatopathological picture database makes it perfect for using AI image recognition skills for assisted diagnosis [ 1 , 2 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

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

Marri¹,

Inamadar²,

Janagond³

et al. 2023

JMIR Dermatol

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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.

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New diagnostic and imaging technologies in dermatology

Cited by 25 publications

References 47 publications

Transforming the treatment of psoriasis to the 21st century: Detecting subclinical therapeutic response to secukinumab using optical coherence tomography as a prognostic indicator

Transforming the treatment of psoriasis to the 21st century: Detecting subclinical therapeutic response to secukinumab using optical coherence tomography as a prognostic indicator

Spatial transcriptomics in human skin research

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

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