ClinicalTrials.gg
Status unknownNCT03362138NNCDUpdated Sep 13, 2018

Artificial Intelligence-assisted Evaluation of Pigmented Skin Lesions

An observational study in Melanoma, Pigmented Skin Lesion and Dysplastic Nevi, sponsored by Assuta Hospital Systems. Status unknown at 1 site in Israel. Open to participants aged 18 Years to 90 Years. Per ClinicalTrials.gov, last updated 2018-09-13.

Primary conditionDysplastic Nevus SyndromeRegistry: MelanomaReclassifiedWhy we changed it
Registry
Melanoma — the first condition term the registry derived for this study.
ClinicalTrials.gg
Dysplastic Nevus Syndrome
Why
  1. The study's listed conditions name Dysplastic Nevus Syndrome, a narrower term within Melanoma.
Confidence
High: the new condition is named in the study's own listed conditions. Of 351 hand-checked studies reclassified this way, 68% were exactly right and 84% in the right disease family; the registry's first term was right for 5% (measured 2026-10-21). How we classify →
Wrong?
Tell us the study's main condition →

Sponsored by Assuta Hospital Systems · Observational

The sponsor has not verified this record recently (last verified Nov 2017), so the status shown — last known as Recruiting — may be out of date.
Study type
Observational
Model
Case-only
Time perspective
Cross-sectional
Enrollment
80
Ages
18 Years to 90 Years
Sex
All
01

Study summary

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.

Read the detailed description

Background

Malignant melanoma (MM) is a deadly cancer, claiming globally about 160000 new cases per year and 48000 deaths [1]. The incidence rate for MM ascended between 1950 and 2007 more than 17-fold in men (1.9 to 33.5 per 100,000) and more than 9-fold in women (2.6 to 25.3 per 100,000) [2]. It is estimated that 76000 new cases and 9000 deaths are diagnosed/ year (US, only, 2017) [3]. European Countries yield 22000/yr. stage 4 melanomas.

MM starts de novo in about 70% of subjects, with a small 2 millimeters or more superficial skin lesion which, if left undiagnosed, might develop into a more advanced stage cancer, followed by silent lymph node spreading and invasion of vital organs such as liver and brain. MM is staged by either direct invasion into the skin, the Breslow scale in mm, or by its skin level involvement, a Clark 1-5 structural skin level of involvement. Both methods of assessment are predictors of long term survival, which is almost unchanged at a Breslow depth beneath 1 mm and much decreased beyond Clark III stage. Therefore early detection of melanoma, generally tagged as small melanoma, is critical.

Present Methodologies of MM detection

The following different methods and diagnostic means for detection of the cutaneous melanoma are currently employed as a means of diagnosis:

  1. Visual recognition by an Asymmetry, Border, Color, Diameter and Evolvement (ABCDE rule). A 30 year old methodology and still running, assessing: ABCDE. It is used by part of US dermatologists who trust their clinical skills and instincts [4]. Due to its innate complexity and myriad of recognition patterns, dermoscopy by epiluminescence microscopy is considered as ineffective by 60% of the non users dermatologists and 30% report it as time consuming, rendering it impracticable. The abovementioned rule was reviewed in 2015 [5] and teaches the use of a diameter greater than 6 mm as a criteria for melanoma recognition. Consequently, it does not contribute to early detection of a melanoma, which might be life saving, since 70-80% of melanomas start de novo as small melanomas. An attempt to change the Diameter criteria, i.e. a decrease in the postulated 6 mm Diameter, leads to a major decrease in sensitivity and specificity, rendering the ABCDE ineffective for early melanoma detection. It is concluded that these basic visual criteria, although widely used, are a non effective prevention method due to their innate criteria which misses the evolving melanomas, since all melanomas start as small melanomas.
  2. Epiluminescence microscopy is a 65 years old art, which is considered the golden standard of evaluation. It is highly dependent on the skills and knowledge of the diagnostician. Epiluminescence microscopy is a the bridge between clinical observation and histopathological diagnosis. It allows visualization of skin pigments up to the papillary dermis and improves detection sensitivity and specificity. Analysis is made by a clinician based on dermoscopy rules [6], which are based on a complicated dermoscopic pattern analysis.

    Complete dermoscopic pattern analysis of a lesion is the mainstream of diagnosis. Each of the dermoscopic patterns can diverge in extent, diameter, general or local appearance, or evolve as multi patterns with various area of expression on the same lesion and rendering the diagnosis beyond an encyclopedic task.

    Due to the complexity of the dermoscopic pattern analysis method, different modifications were proposed, such as a simplified ABCD rule, an intermediate 7 rules assessment and a 11 rules checklist. The myriad of data and its interpretation render even experts in dermoscopy to identify melanomas with a relative low ratio from 5:1 to 15:1, i.e. the number of biopsies of benign lesions performed in order to make the diagnosis of one skin melanoma.

    Furthermore, there are claims that one diagnostic sign might be more sensitive that other signs, alike the criteria of a light brown structureless area. Dermatologists commonly fail in diagnosing early small melanoma which is the critical period of growth of the tumor and most efficacious prevention.

  3. Visual recognition by temporal total body photography is yet non standardized methodology, a time consuming and patient expensive procedure. It uses the human comparison assessment and is subjected to biases of pixel photo during performance of the measurement, due to lighting, background and camera position. Addition of patient assessment by artificial intelligence, an analysis based on computer evaluation of different colorimetric and geometric parameters of a lesion in real time, confer a limited advantage only, yielding sometimes more false positives compared to inexperienced or even experienced clinicians.
  4. Multi Spectrometry devices, which provide measurements of melanin, collagen and hemoglobin with further use of image analysis, do not confer any advantage to epiluminescence microscopy. Although displaying a higher sensitivity, these methodologies specificity are considerably below that of simple epiluminescence microscopy in melanoma identification rate .
  5. Confocal scanning microscopy, a procedure which uses a low power laser for a 3D imaging. An effective means of diagnosis. However, the use of this methodology is limited by its high cost, a prolonged (6 mo) learning curve of 2000-4000 images, and the requirement for a highly specialized and trained personal, since this methodology is highly subjected to artifacts.
  6. Experimental under development methods: alike Electrical impedance spectroscopy, Optical coherence tomography, High frequency ultrasound, Laser doppler perfusion imaging, Dynamic thermal imaging, Photoacoustic microscopy, to be further developed and proved.

Clinical Requirements

It is desirable to identify a new non invasive methodology which will

  1. Improve Sensitivity of diagnosis of pigmented skin lesions, alike dysplastic nevi, Spitz nevi and MM
  2. Identify small MM,
  3. Increase Specificity of lesions to be biopsied
  4. Non invasive fast technology.

Common Practice

Commonly, a patient is evaluated by a physician due to either a skin lesion complaint or, with high risk patients, a summon to visit on a regular basis.

Only some of the dermatologists employ dermoscopy or are skilled to use it since it may seem to be time consuming. There is no international standard of degree of skill. Even a golden standard textbook of dermoscopy is not existent.

Upon recognition of suspicious lesion, patient is referred to a surgeon for biopsy.

As recently reviewed and in view of the above-mentioned, the incidence of melanoma is continuing to increase. All present methods commonly do not diagnose melanoma at an early stage and epiluminescence microscopy is highly user dependant and commonly misses the diagnosis . Early excision is the only strategy to reduce the death toll associated with melanoma. Unnecessary excision of benign lesions increases morbidity and raise healthcare costs associated with melanoma screening, resulting in recommended restrictions to a total body screening by Surgeon General (2016) and questioning the efficacy of such a screening.

The requirement for a more systemized methodology and system is obvious in view of the expected increase in melanoma incidence to a 1:15 within the next 15 years. A new device able to capture the window of opportunity of Dysplastic Nevus to small melanoma is highly useful for today healthcare systems.

Primary Study Objectives

  • A Sensitivity of at least 75% for Classifier results as compared to biopsy Sensitivity is the percentage of correctly diagnosed dysplastic nevi, Spitz nevi or melanomas. [ Designated as safety issue: No ].
  • A Sensitivity of at least 85% for an Modified Classifier Technology (MCT) results as compared to biopsy. Sensitivity is the percentage of correctly diagnosed dysplastic nevi, Spitz nevi or melanomas. [ Designated as safety issue: No ]
  • A Specificity of at least 33% for Classifier results as compared to biopsy. Specificity is the percentage of correctly identified dysplastic nevi, Spitz nevi or melanomas. [ Designated as safety issue: No ]
  • A Specificity of at least 33% for MCT results as compared to biopsy. Specificity is the percentage of correctly identified dysplastic nevi, Spitz nevi or melanomas. [ Designated as safety issue: No ]

Secondary Study Objectives

  • The positive predictive value of MCT, compared to the biopsy result [ Designated as safety issue: No ]
  • The negative predictive value of MCT, [ Designated as safety issue: No ]
  • The false positive rate of MCT, [ Designated as safety issue: No ]
  • The false negative rate of MCT, [ Designated as safety issue: No ]
  • The difference between two dermoscopes, [ Designated as safety issue: No ]
  • The positive predictive value of Classifier, compared to the biopsy result [ Designated as safety issue: No ]
  • The negative predictive value of Classifier, [ Designated as safety issue: No ]
  • The false positive rate of Classifier, [ Designated as safety issue: No ]
  • The false negative rate of Classifier, [ Designated as safety issue: No ]
  • Clinical decision Sensitivity and Specificity rates.
02

Conditions studied

  • Melanoma
  • Pigmented Skin Lesion
  • Dysplastic Nevi

Browse trials for

Keywords

  • melanoma
  • dysplastic nevus
  • artificial intelligence
  • early diagnosis
03

In context

Dysplastic Nevus Syndrome Reclassified

24 studies on the registry are indexed under Dysplastic Nevus Syndrome; 7 are open to participants now.

This study's planned enrollment of 80 is below the median of 283 across 16 observational studies indexed under Dysplastic Nevus Syndrome.

Browse Dysplastic Nevus Syndrome studies →

Lead sponsor

Assuta Hospital Systems is the lead sponsor of 34 studies on the registry; none are open to participants now.

Compared within Dysplastic Nevus Syndrome, this study's primary condition as ClinicalTrials.gg classifies it; the registry's first condition term is Melanoma. The counts are of registry records indexed under each term, refreshed daily. How we classify →

04

Who can participate

Ages eligible
18 Years to 90 Years
Sexes eligible
All
Sampling method
Non-probability sample

Study population

Subjects examined at a Dermatologic speciality clinic.

Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.

Inclusion criteria

  • Patient aged 18-90 years
  • A pigmented lesion by dermoscopy.
  • Clinical management by the examining dermatologist results in
  • The diameter of the pigmented area is between 1 and 40 millimeters
  • The patient has consented to participate in the study and has signed the Form

Exclusion criteria

Exclusion Criteria:

  • Non intact skin (ulcers, bleeding)
  • The lesion is located within 1 cm of the eye
  • The lesion is located on mucosal surfaces
  • The lesion is on or under nails
Terms in these criteria, in plain language(2)

Underlined terms in the criteria open these too. Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.

Biopsy and archival tissue

A biopsy takes a small piece of tissue to examine. “Archival tissue” is a sample already taken, for example at diagnosis, and kept by the hospital.

Whether a stored sample will do depends on when it was taken, how much there is and what the study needs. Ask whether the study accepts your stored sample or needs a new biopsy.

Informed consent

Before you join, the study team explains what taking part involves — its purpose, what happens, the risks and possible benefits — and answers your questions, so you can decide freely. Consent is usually recorded on a form (the informed consent form, ICF) signed by you or, where the law allows, by someone legally authorised to decide for you.

You can keep asking questions during the study, you are told of new information that matters, and you can choose to leave at any time.

05

Study design

Observational model
Case-only
Time perspective
Cross-sectional
Enrollment
80 participants (estimated)
Patient registry
No
Biospecimen retention
Samples without dna

Groups and cohorts

  • Dermoscopy

    Dermoscopic imaging of a lesion decided to be biopsied

    Device: dermoscopy

Interventions

  • Devicedermoscopy

    Solely after the dermatologist has decided to biopsy a lesion and sent the patient to biopsy, a dermoscopic image is captured by a camera attached to a dermoscope.

06

What researchers measure

Primary outcomes

  1. Sensitivity for Classifier results as compared to biopsy

    A Sensitivity of at least 75% for Classifier results as compared to biopsy

    Time frame: 15 months

  2. Sensitivity for MCT results as compared to biopsy

    A Sensitivity of at least 85% for Classifier results as compared to biopsy

    Time frame: 15 months

Secondary outcomes

  1. The positive predictive value of MCT

    The positive predictive value of MCT, compared to the biopsy result

    Time frame: 15 months

  2. The negative predictive value of MCT

    The negative predictive value of MCT, compared to the biopsy result

    Time frame: 15 months

07

Study locations

1 of 1 sites recruiting
  • Maccabi Healthcare Clinic
    Tel Aviv, 59485, Israel
    Recruiting
08

References and documents

Publications

  • Eggermont AM, Spatz A, Robert C. Cutaneous melanoma. Lancet. 2014 Mar 1;383(9919):816-27. doi: 10.1016/S0140-6736(13)60802-8. Epub 2013 Sep 19. PubMed 24054424 ↗
  • Mayer JE, Swetter SM, Fu T, Geller AC. Screening, early detection, education, and trends for melanoma: current status (2007-2013) and future directions: Part I. Epidemiology, high-risk groups, clinical strategies, and diagnostic technology. J Am Acad Dermatol. 2014 Oct;71(4):599.e1-599.e12; quiz 610, 599.e12. doi: 10.1016/j.jaad.2014.05.046. PubMed 25219716 ↗
  • Siegel R, Naishadham D, Jemal A. Cancer statistics, 2012. CA Cancer J Clin. 2012 Jan-Feb;62(1):10-29. doi: 10.3322/caac.20138. Epub 2012 Jan 4. PubMed 22237781 ↗
  • Noor O 2nd, Nanda A, Rao BK. A dermoscopy survey to assess who is using it and why it is or is not being used. Int J Dermatol. 2009 Sep;48(9):951-2. doi: 10.1111/j.1365-4632.2009.04095.x. PubMed 19702978 ↗
  • American Academy of Dermatology Ad Hoc Task Force for the ABCDEs of Melanoma; Tsao H, Olazagasti JM, Cordoro KM, Brewer JD, Taylor SC, Bordeaux JS, Chren MM, Sober AJ, Tegeler C, Bhushan R, Begolka WS. Early detection of melanoma: reviewing the ABCDEs. J Am Acad Dermatol. 2015 Apr;72(4):717-23. doi: 10.1016/j.jaad.2015.01.025. Epub 2015 Feb 16. PubMed 25698455 ↗
  • Campos-do-Carmo G, Ramos-e-Silva M. Dermoscopy: basic concepts. Int J Dermatol. 2008 Jul;47(7):712-9. doi: 10.1111/j.1365-4632.2008.03556.x. PubMed 18613881 ↗
  • Dascalu A, David EO. Skin cancer detection by deep learning and sound analysis algorithms: A prospective clinical study of an elementary dermoscope. EBioMedicine. 2019 May;43:107-113. doi: 10.1016/j.ebiom.2019.04.055. Epub 2019 May 14. PubMed 31101596 ↗
  • Walker BN, Rehg JM, Kalra A, Winters RM, Drews P, Dascalu J, David EO, Dascalu A. Dermoscopy diagnosis of cancerous lesions utilizing dual deep learning algorithms via visual and audio (sonification) outputs: Laboratory and prospective observational studies. EBioMedicine. 2019 Feb;40:176-183. doi: 10.1016/j.ebiom.2019.01.028. Epub 2019 Jan 20. PubMed 30674442 ↗
09

Timeline

Tracking since Sep 25, 2026
ElapsedEstimatedEstimated, not yet reached

Started Dec 2017 (actual). Primary completion is estimated for Dec 2018, 12 months after the start.

How these figures are worked out, and which studies are left out of them.

Registered
Nov 27, 2017
First posted
Dec 5, 2017
StartActual
Dec 6, 2017
Primary completionEst.
Dec 31, 2018
CompletionEst.
Mar 31, 2019
Last update
Sep 13, 2018
What these dates mean
Start.
When the first participant enrolled; while estimated, when the study expects to open to recruitment.
Primary completion.
When the last participant was examined or treated to collect data for the study's main question, its primary outcome. A study that stopped early gives the date its data collection stopped.
Completion.
When the last participant's last visit took place, for every outcome, secondary ones and side effects included: the end of the study. Often later than primary completion, sometimes the same day.
Actual and Est.
Actual means the registry says the date has happened. Est. (estimated) is a date the sponsor plans or expects. It can still move, and the sponsor is expected to change it to actual once it has passed.
No changes since tracking began. The registry record was last updated on Sep 13, 2018, before this site started recording changes on Sep 25, 2026. Its history is on ClinicalTrials.gov ↗
10

Registry details

Key details

Study ID
NCT03362138
Lead sponsor
Assuta Hospital Systems
Responsible party
Sponsor
First posted
Dec 5, 2017
Start date
Dec 6, 2017
Primary completion
Dec 31, 2018 (estimated)
Completion
Mar 31, 2019 (estimated)
Last update
Sep 13, 2018

Study contacts

Avi Dascalu, MD, Ph.D.
Contact
dasc17@gmail.com
+972544306331
Robert Raleigh, MBA
Contact
rraleigh@braintreelabs.com
+781-348-0707
Avi Dascalu, MD. Ph.D.
principal investigator · Bostel LLC

Oversight

Data monitoring committee
No
FDA-regulated drug
No
FDA-regulated device
No
View the source record on ClinicalTrials.gov ↗

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