Area of research
Oncology · Biomedical Engineering
Research interest
Research interests include Cutaneous Melanoma Detection and Management, Optical Coherence Tomography Applications, Nonmelanoma Skin Cancer Studies, and Cutaneous lymphoproliferative disorders research.
Benign or Malignant? Ex Vivo Confocal Laser Scanning Microscopy for Bedside Histological Assessment of Melanocytic Lesions
Unveiling the hidden boundaries: AI‐assisted line‐field optical coherence tomography margin mapping for precise excision of basal cell carcinoma – A step‐by‐step tutorial
Patients’ and dermatologists’ preferences in artificial intelligence–driven skin cancer diagnostics: A prospective multicentric survey study
Line‐field confocal optical coherence tomography in lichen planopilaris and frontal fibrosing alopecia: A pilot study
Line‐field confocal optical coherence tomography: Characteristic hints for the diagnosis of scarring alopecia due to lupus erythematodes: A preliminary study
Update of penetrance estimates in Birt-Hogg-Dubé syndrome
Innovation in Actinic Keratosis Assessment: Artificial Intelligence-Based Approach to LC-OCT PRO Score Evaluation
Line-Field Confocal Optical Coherence Tomography Increases the Diagnostic Accuracy and Confidence for Basal Cell Carcinoma in Equivocal Lesions: A Prospective Study
Ex vivo confocal laser scanning microscopy: A diagnostic technique for easy real‐time evaluation of benign and malignant skin tumours
Line‐field optical coherence tomography: <i>in vivo</i> diagnosis of basal cell carcinoma subtypes compared with histopathology
In-Vivo LC-OCT Evaluation of the Downward Proliferation Pattern of Keratinocytes in Actinic Keratosis in Comparison with Histology: First Impressions from a Pilot Study
Line‐field confocal optical coherence tomography for the in vivo real‐time diagnosis of different stages of keratinocyte skin cancer: a preliminary study
Machine Learning Based Prediction of Squamous Cell Carcinoma in Ex Vivo Confocal Laser Scanning Microscopy
Ex‐vivo fluorescence confocal microscopy with digital staining for characterizing basal cell carcinoma on frozen sections: A comparison with histology
In vivo examination of healthy human skin after short‐time treatment with moisturizers using confocal Raman spectroscopy and optical coherence tomography: Preliminary observations
Man against machine reloaded: performance of a market-approved convolutional neural network in classifying a broad spectrum of skin lesions in comparison with 96 dermatologists working under less artificial conditions
Artificial Intelligence and Its Effect on Dermatologists’ Accuracy in Dermoscopic Melanoma Image Classification: Web-Based Survey Study
Skin lesions of face and scalp – Classification by a market-approved convolutional neural network in comparison with 64 dermatologists
Superior skin cancer classification by the combination of human and artificial intelligence
Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks
Comparing artificial intelligence algorithms to 157 German dermatologists: the melanoma classification benchmark