Studies

Total Body Mapping

PubMed – 2024

Performance of an automated total body mapping algorithm to detect melanocytic lesions of clinical relevance

Winkler et al. (2024)

Dermatology Practical & Conceptual – 2022

Indications for Digital Monitoring of Patients with Multiple Nevi

Recommendations from the International Dermoscopy Society

Russo et al. (2022)

PubMed – 2021

The Value of Total Body Photography for the Early Detection of Melanoma

A Systematic Review

Hornung et al. (2021)

PubMed – 2020

The importance of total-body photography

and sequential digital dermatoscopy for monitoring patients at increased melanoma risk

Deinlein et al. (2020)

PubMed – 2019

Usefulness of the 'two-step method'

of digital follow-up for early-stage melanoma detection in high-risk French patients: a retrospective 4-year study

Gasparini et al. (2019)

PubMed 2012

Benefits of total body photography and digital dermatoscopy

(‘‘two-step method of digital follow-up’’) in the early diagnosis of melanoma in patients at high risk for melanoma

Salerni et al. (2012)

PubMed – 2012

Total body skin examination

for skin cancer screening in patients with focused symptoms

Argenziano et al. (2012)

PubMed – 2010

Comparative analysis

of total body vs. dermatoscopic photographic monitoring of nevi in similar patient populations at risk for cutaneous melanoma

Goodson et al. (2010)

Studies

Artificial Intelligence

Sage Journals – 2023

Using Artificial Intelligence as a Melanoma Screening Tool in Self-Referred Patients

M. Crawford, P. Hull et al. (2023)

JAMA Network - 2023

Human With Machine

Assessment of Diagnostic Performance of Dermatologists Cooperating With a Convolutional Neural Network in a Prospective Clinical Study

Winkler et al. (2023)

ScienceDirect – 2023

Observational study

Investigating the level of support from a convolutional neural network in face and scalp lesions deemed diagnostically ‘unclear’ by dermatologists

K. S. Kommoss at al. (2023)

EJC – 2022

Does sex matter?

Analysis of sex-related differences in the diagnostic performance of a market-approved convolutional neural network for skin cancer detection

Sies et al. (2022)

PubMed – 2021

Skin lesions of face and scalp

Classification by a market-approved convolutional neural network in comparison with 64 dermatologists

Haenssle et al. (2021)

ScienceDirect – 2021

Original Research

Association between different scale bars in dermoscopic images and diagnostic performance of a market-approved deep learning convolutional neural network for melanoma recognition

Winkler et al. (2021)


 

ScienceDirect – 2020

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

Haenssle et al. (2020)

PubMed – 2020

Melanoma recognition by a deep learning convolutional neural network

Performance in different melanoma subtypes and localisations.

Winkler et al. (2020)

ScienceDirect – 2020

The Use of Non-Invasive Imaging Techniques in the Diagnosis of Melanoma

A Prospective Diagnostic Accuracy Study

MacLellan et al. (2020)

Nature Medicine – 2020

Human–computer collaboration for skin cancer recognition

Tschandl  et al. (2020)

PubMed – 2020

Past and present of computer-assisted dermoscopic diagnosis

Performance of a conventional image analyser versus a convolutional neural network in a prospective data set of 1,981 skin lesion.

Sies et al. (2020)

PubMed – 2020

Diagnostic performance of a deep learning convolutional neural network in the differentiation of combined naevi and melanomas

Haenssle et al. (2020)

PubMed – 20218

Man against machine

Haenssle et al. (2018)