Denna sida har översatts automatiskt och översättningens korrekthet kan inte garanteras. Vänligen se engelsk version för en källtext.

Machine Learning Analysis of Two-photon Fluorescence Microscopy of Dermatologic Biopsies

15 augusti 2026 uppdaterad av: Michael Giacomelli, University of Rochester

Machine Learning Analysis of Expanded Two-photon Imaging of Skin Biopsy Specimens

The goal of this study is to investigate the ability of a machine learning model to evaluate two-photon fluorescence microscopy images of dermatologic biopsies at point of care.

The main question it aims to answer is:

• How well do two-photon fluorescence images of biopsies taken in a clinic and evaluated by a machine learning model agree with conventional histology?

Studieöversikt

Status

Rekrytering

Betingelser

Intervention / Behandling

Detaljerad beskrivning

This study will image biopsy specimens at point of care using two-photon fluorescence microscopy (TPFM) and then assess how well the images predict the eventual clinical diagnosis using a machine learning model. Because two-photon images can be acquired from small biopsy specimens within minutes of excision, they could potentially be used to immediately diagnose patients, but the accuracy of TPFM for various skin conditions is unknown.

Individual biopsy specimens in a dermatology clinic will be imaged using TPFM shortly after biopsy procedures. Immediately following imaging, a machine learning model will evaluate the TPFM images then compute a confidence score for a diagnosis of basal cell carcinoma (BCC), squamous cell carcinoma, and non-cancer. The relative confidence in each diagnosis will be compared, and if sufficient confidence is achieved, the model will render a diagnosis or else flag the specimen as indeterminate for manual pathologist review. This workflow will evaluate the use of ML + TPFM to perform point of care diagnosis of skin lesions.

Following TPFM imaging, the specimen will be submitted for histological processing, which will guide actual patient treatment. Following conclusion of patient treatment, the resulting histology slides will be scanned for comparison and the final patient diagnosis recorded. Images of the histology slides will be read by a pathologist to establish a gold-standard diagnosis. The official diagnosis and the diagnosis from the collaborating pathologist will be compared.

Patient treatment will still be decided by conventional histopathology. TPFM will not be used to change treatment.

Studietyp

Interventionell

Inskrivning (Beräknad)

92

Fas

  • Inte tillämpbar

Kontakter och platser

Det här avsnittet innehåller kontaktuppgifter för dem som genomför studien och information om var denna studie genomförs.

Studiekontakt

Studieorter

Deltagandekriterier

Forskare letar efter personer som passar en viss beskrivning, så kallade behörighetskriterier. Några exempel på dessa kriterier är en persons allmänna hälsotillstånd eller tidigare behandlingar.

Urvalskriterier

Åldrar som är berättigade till studier

  • Barn
  • Vuxen
  • Äldre vuxen

Tar emot friska volontärer

Nej

Beskrivning

Inclusion Criteria:

  • Punch, excisional or shave biopsy specimen

Exclusion Criteria:

  • Biopsy indication includes melanoma or dysplastic/atypical nevus
  • Excision thickness of less than 1 mm
  • Excision longest dimension less than 2 mm
  • Excision performed as multiple pieces in a single specimen container

Studieplan

Det här avsnittet ger detaljer om studieplanen, inklusive hur studien är utformad och vad studien mäter.

Hur är studien utformad?

Designdetaljer

  • Primärt syfte: Diagnostisk
  • Tilldelning: N/A
  • Interventionsmodell: Enskild gruppuppgift
  • Maskning: Ingen (Open Label)

Vapen och interventioner

Deltagargrupp / Arm
Intervention / Behandling
Experimentell: TPFM imaging of biopsy
Specimens will be imaged with TPFM and diagnosed using a machine learning model
Enhet: Two photon microscopy imaging
Ex vivo tissues will be imaged with two-photon microscopy and analyzed with machine learning for diagnosis

Vad mäter studien?

Primära resultatmått

Resultatmått
Åtgärdsbeskrivning
Tidsram
Sensitivity of Machine Learning Analysis of Two Photon Fluorescence Microscopy Images At Point of Care
Tidsram: During or immediately following patient biopsy (same day)
A machine learning model will evaluate TPFM images of patient biopsies at point of care. Sensitivity will be calculated for the machine learning model using two photon fluorescence microscopy images. Sensitivity is defined as the number of true positive diagnoses divided by the sum of true positive and false negative diagnoses among biopsy specimens for which the machine learning model provides a definitive diagnosis. The patient's ultimate clinical diagnosis will serve as the reference standard.
During or immediately following patient biopsy (same day)
Specificity of Machine Learning Analysis of Two Photon Fluorescence Microscopy Images At Point of Care
Tidsram: During or immediately following patient biopsy (same day)
A machine learning model will evaluate TPFM images of patient biopsies at point of care. Specificity will be calculated for the machine learning model using two photon fluorescence microscopy images. Specificity is defined as the number of true negative diagnoses divided by the sum of true negative and false positive diagnoses among biopsy specimens for which the machine learning model provides a definitive diagnosis. The patient's ultimate clinical diagnosis will serve as the reference standard.
During or immediately following patient biopsy (same day)

Sekundära resultatmått

Resultatmått
Åtgärdsbeskrivning
Tidsram
Proportion of Discordant Diagnoses Attributable to Machine Learning Model Interpretation Errors
Tidsram: After completion of patient diagnosis (typically 1-2 weeks after procedure)
For biopsy specimens with discordant diagnoses between the machine learning model and the patient's ultimate clinical diagnosis, a dermatopathologist will review each case and classify the source of disagreement as machine learning model interpretation error, image quality limitation, or image coregistration error. The proportion of discordant diagnoses attributable to each source of disagreement will be reported.
After completion of patient diagnosis (typically 1-2 weeks after procedure)
Proportion of Biopsy Specimens With a Definitive Machine Learning Diagnosis
Tidsram: During or immediately following patient biopsy (same day)
The proportion of biopsy specimens for which the machine learning model provides a definitive diagnosis based on two photon fluorescence microscopy images will be calculated as the number of specimens receiving a definitive diagnosis divided by the total number of specimens evaluated.
During or immediately following patient biopsy (same day)

Samarbetspartners och utredare

Det är här du hittar personer och organisationer som är involverade i denna studie.

Sponsor

University of Rochester

Samarbetspartners

National Cancer Institute (NCI)
Rochester Dermatologic Surgery

Studieavstämningsdatum

Dessa datum spårar framstegen för inlämningar av studieposter och sammanfattande resultat till ClinicalTrials.gov. Studieposter och rapporterade resultat granskas av National Library of Medicine (NLM) för att säkerställa att de uppfyller specifika kvalitetskontrollstandarder innan de publiceras på den offentliga webbplatsen.

Studera stora datum

Studiestart (Faktisk)

24 juni 2026

Primärt slutförande (Beräknad)

1 juni 2027

Avslutad studie (Beräknad)

1 juli 2027

Studieregistreringsdatum

Först inskickad

25 juni 2026

Först inskickad som uppfyllde QC-kriterierna

30 juni 2026

Första postat (Faktisk)

6 juli 2026

Uppdateringar av studier

Senaste uppdatering publicerad (Faktisk)

19 augusti 2026

Senaste inskickade uppdateringen som uppfyllde QC-kriterierna

15 augusti 2026

Senast verifierad

1 augusti 2026

Mer information

Termer relaterade till denna studie

Nyckelord

Ytterligare relevanta MeSH-villkor

Andra studie-ID-nummer

  • STUDY00009823B
  • R37CA258376 (U.S.S. NIH-anslag/kontrakt)

Plan för individuella deltagardata (IPD)

Planerar du att dela individuella deltagardata (IPD)?

JA

IPD-planbeskrivning

Deidentified sets of two-photon images and corresponding conventional histology will be made available upon request. Links to full resolution image data will be included in publications along with the results of machine learning analysis.

IPD-delning som stöder informationstyp

  • STUDY_PROTOCOL

Läkemedels- och apparatinformation, studiedokument

Studerar en amerikansk FDA-reglerad läkemedelsprodukt

Nej

Studerar en amerikansk FDA-reglerad produktprodukt

Ja

produkt tillverkad i och exporterad från U.S.A.

Nej

Denna information hämtades direkt från webbplatsen clinicaltrials.gov utan några ändringar. Om du har några önskemål om att ändra, ta bort eller uppdatera dina studieuppgifter, vänligen kontakta register@clinicaltrials.gov. Så snart en ändring har implementerats på clinicaltrials.gov, kommer denna att uppdateras automatiskt även på vår webbplats .

Prenumerera