Machine Learning Analysis of Two-photon Fluorescence Microscopy of Dermatologic Biopsies
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?
연구 개요
상세 설명
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.
연구 유형
등록 (추정된)
단계
- 해당 없음
연락처 및 위치
연구 연락처
- 이름: Michael Giacomelli, Ph.D
- 전화번호: 5852766260
- 이메일: mgiacome@ur.rochester.edu
연구 장소
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New York
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Victor, New York, 미국, 14654
- 모병
- Rochester Dermatologic Surgery
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연락하다:
- Sherrif Ibrahim, M.D.-Ph.D.
- 전화번호: 585-222-1400
- 이메일: dr.ibrahim@rochesterdermsurgery.com
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참여기준
자격 기준
공부할 수 있는 나이
- 어린이
- 성인
- 고령자
건강한 자원 봉사자를 받아들입니다
설명
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
공부 계획
연구는 어떻게 설계됩니까?
디자인 세부사항
- 주 목적: 특수 증상
- 할당: 해당 없음
- 중재 모델: 단일 그룹 할당
- 마스킹: 없음(오픈 라벨)
무기와 개입
참가자 그룹 / 팔 |
개입 / 치료 |
|---|---|
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실험적: TPFM imaging of biopsy
Specimens will be imaged with TPFM and diagnosed using a machine learning model
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Ex vivo tissues will be imaged with two-photon microscopy and analyzed with machine learning for diagnosis
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연구는 무엇을 측정합니까?
주요 결과 측정
결과 측정 |
측정값 설명 |
기간 |
|---|---|---|
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Sensitivity of Machine Learning Analysis of Two Photon Fluorescence Microscopy Images At Point of Care
기간: During or immediately following patient biopsy (same day)
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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.
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During or immediately following patient biopsy (same day)
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Specificity of Machine Learning Analysis of Two Photon Fluorescence Microscopy Images At Point of Care
기간: During or immediately following patient biopsy (same day)
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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.
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During or immediately following patient biopsy (same day)
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2차 결과 측정
결과 측정 |
측정값 설명 |
기간 |
|---|---|---|
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Proportion of Discordant Diagnoses Attributable to Machine Learning Model Interpretation Errors
기간: After completion of patient diagnosis (typically 1-2 weeks after procedure)
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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.
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After completion of patient diagnosis (typically 1-2 weeks after procedure)
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Proportion of Biopsy Specimens With a Definitive Machine Learning Diagnosis
기간: During or immediately following patient biopsy (same day)
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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.
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During or immediately following patient biopsy (same day)
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공동 작업자 및 조사자
연구 기록 날짜
연구 주요 날짜
연구 시작 (실제)
기본 완료 (추정된)
연구 완료 (추정된)
연구 등록 날짜
최초 제출
QC 기준을 충족하는 최초 제출
처음 게시됨 (실제)
연구 기록 업데이트
마지막 업데이트 게시됨 (실제)
QC 기준을 충족하는 마지막 업데이트 제출
마지막으로 확인됨
추가 정보
이 연구와 관련된 용어
추가 관련 MeSH 약관
기타 연구 ID 번호
- STUDY00009823B
- R37CA258376 (미국 NIH 보조금/계약)
개별 참가자 데이터(IPD) 계획
개별 참가자 데이터(IPD)를 공유할 계획입니까?
IPD 계획 설명
IPD 공유 지원 정보 유형
- 연구_프로토콜
약물 및 장치 정보, 연구 문서
미국 FDA 규제 의약품 연구
미국 FDA 규제 기기 제품 연구
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