NCT03362138: NNCD

Artificial Intelligence-assisted Evaluation of Pigmented Skin Lesions

Status unknown Last updated 13 September 2018

What this trial tests

trial testing dermoscopy in Melanoma in 80 participants. Status unknown.

Timeline

6 December 2017

Primary endpoint
31 December 2018

31 March 2019

Quick facts

Lead sponsor	Assuta Hospital Systems
Status	Status unknown
Study type	OBSERVATIONAL
Enrollment	80
Start date	6 December 2017
Primary completion	31 December 2018
Estimated completion	31 March 2019
Sites	1 location across Israel

Drugs / interventions tested

• dermoscopy

Conditions studied

• Melanoma — all drugs for Melanoma →
• Pigmented Skin Lesion — all drugs for Pigmented Skin Lesion →
• Dysplastic Nevi — all drugs for Dysplastic Nevi →

Sponsor

Assuta Hospital Systems

Who can join

Adults 18 to 90, any sex, with Melanoma or Pigmented Skin Lesion. Patients with the condition only — healthy volunteers not accepted.

Sponsor's own description

Malignant melanoma (MM) is a deadly cancer, claiming globally about 160000 new cases per year and 48000 deaths at a 1:28 lifetime incidence (2016).

The golden standard, dermoscopy, enables Dermatologists to diagnose with a sensitivity of 40%, and a 8-12% specificity, approximately. Additional diagnostic abilities are restricted to devices which are either unproved or experimental.

A new technology of Neuronal Network Clinical Decision Support (NNCD) was developed. It uses a dermoscopic imaging device and a camera able to capture an image. The photo is transferred to a Cloud Server and further analyzed by a trained classifier. Classifier training is aimed at a high accuracy diagnosis of Dysplastic Nevi (DN), Spitz Nevi and Malignant Melanoma detection with assistance from a Deep Neuronal Learning network (DLN). Diagnosis output is an excise or do not excise recommendation for pigmented skin lesions.

A total of 80 subjects already referred to biopsy pigmented skin lesions will be examined by dermoscopy imaging in a non interventional study. Artificial Intelligence output results, as measured by 2 different dermoscopes, to be compared to ground truth biopsies, by either classifier decisions or a novel Modified Classifier Technology output decisions.

Primary endpoints are sensitivity and specificity detection of the NNCD techniques. Secondary endpoints are the positive and negative prediction ratios of NNCD techniques.

Publications & conference data

3 peer-reviewed publications reference this trial (live from Europe PMC):

1. Skin cancer detection by deep learning and sound analysis algorithms: A prospective clinical study of an elementary dermoscope.
   Dascalu A, David EO. · · 2019 · cited 35× · PMID 31101596 · DOI 10.1016/j.ebiom.2019.04.055
2. Dermoscopy diagnosis of cancerous lesions utilizing dual deep learning algorithms via visual and audio (sonification) outputs: Laboratory and prospective observational studies.
   Walker BN, Rehg JM, Kalra A, Winters RM, et al · · 2019 · cited 18× · PMID 30674442 · DOI 10.1016/j.ebiom.2019.01.028
3. Non-melanoma skin cancer diagnosis: a comparison between dermoscopic and smartphone images by unified visual and sonification deep learning algorithms.
   Dascalu A, Walker BN, Oron Y, David EO. · · 2022 · cited 16× · PMID 34546412 · DOI 10.1007/s00432-021-03809-x