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  3. Body Region Classifier for Teledermatology Body Region Classifier for Teledermatology

Body Region Classifier for Teledermatology

Automated validation of visible body regions in teledermatology pictures.

Brief information

School:

Computer Science and Information Technology

Status:

Completed

Period:

01.11.2022 - 03.11.2023

Overview

Derma2go offers a teledermatology web-based service connecting patients and dermatologists. Patients can submit information about their skin lesions (not limited to skin cancer) and overall health condition. They receive in return diagnosis and treatment recommendations from certified dermatologists. This service is offered in Switzerland, Germany, Austria and is refunded by German private medical insurances.

One of the main challenges for derma2go is to ensure that patients provide all relevant information necessary for diagnosis. When information is missing, supplementary patient-to-dermatologist exchanges are required, decreasing the efficiency of the process and negatively affecting user experience for both, patient and dermatologists. Moreover, due to the very limited number of certified dermatologists, any process inefficiencies hamper scalability and further growth of derma2go.

To better guide the patients in the information submission phase, fully automated verification steps are therefore required. This is especially difficult with uploaded images. Our vision is to develop a machine learning algorithm to automatically identify whether a patient has uploaded images of all relevant body regions (the list being determined based on patient’s questionnaire answers).

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Facts

Type of project

Forschung

Internal organisations involved
  • Algorithmic Business F&E
Funding
  • Innosuisse - HSLU als Hauptforschungspartnerin (Main Research Partner)
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Links

  • Derma2go

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Persons involved: internal

Project manager
  • Ludovic Amruthalingam
Project Co-Head
  • Marc Pouly
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Persons involved: external

External project manager
  • Christian Greis
External Project Co-Head
  • Vahid Djamei

Publications

  • Article, review; peer reviewed (2)

    • Gottfrois, Philippe Léon-Marius; Jie, Zhu; Steiger, Alexandra; Amruthalingam, Ludovic; Kind, Andre; Heinzelmann, Viola; Mang, Claudia; Mueller, Simon & Navarini, Alexander (2024). AI-powered visual diagnosis of vulvar lichen sclerosus: A pilot study. Journal of European Academy of Dermatology Venereology, 2024, doi: 10.1111/jdv.20306

    • Amruthalingam, Ludovic; Gökduman, Bulus; Gonzalez-Jimenez, Alvaro; Gottfrois, Philippe Léon-Marius; Koller, Thomas; Kunz, Michael; Navarini, Alexander & Pouly, Marc (2022). Improved Diagnosis by Automated Macro- and Micro-anatomical Region Mapping of Skin Photographs. Journal of European Academy of Dermatology Venereology, 2022, 10. doi: 10.1111/jdv.18476

  • Theses (Bachelor/Master/Dissertation/Habilitation) (1)

    • Amruthalingam, Ludovic (2022). Deep Learning in Clinical Dermatology. Dissertation, Universität Basel, Basel, Schweiz.

Brief information

School:

Computer Science and Information Technology

Status:

Completed

Period:

11/01/2022 - 11/03/2023

Project Head

Dr. Ludovic Amruthalingam

Senior Research Associate

+41 41 349 30 74

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Project Co-Head

Prof. Dr. Marc Pouly

Co-Head Applied AI Research Lab

+41 41 757 68 25

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Lucerne University of Applied Sciences and Arts


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+41 41 228 42 42

info@hslu.ch

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