Machine Learning Analysis of Two-photon Fluorescence Microscopy of Dermatologic Biopsies
Ориентир для пациента и семьи
Простыми словами
Автоматическая сводка по структурированным данным реестра. Она помогает сориентироваться, но не заменяет официальный протокол или оценку врача.
- Что изучают
- В протоколе указаны: Two photon microscopy imaging.
- Кому может быть актуально
- Состояния в реестре: Basal Cell Carcinoma of Skin, Squamous Cell Carcinoma (Skin). Базовые параметры: Без ограничений · Все.
- Что важно проверить
- Возраст, диагноз и пол — только базовые ориентиры. Предыдущее лечение, анализы и другие обязательные условия указаны ниже в критериях участия.
- Где проводится
- США
- Следующий шаг
- Сохраните исследование, покажите его лечащему врачу и уточните актуальный статус у исследовательского центра. Расходы, документы и поездка →
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Официальное название
Machine Learning Analysis of Expanded Two-photon Imaging of Skin Biopsy Specimens
Обзор
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?
Подробное описание
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.
Вмешательства
- Устройство Two photon microscopy imaging
Ex vivo tissues will be imaged with two-photon microscopy and analyzed with machine learning for diagnosis
Первичные конечные точки
- Sensitivity of Machine Learning Analysis of Two Photon Fluorescence Microscopy Images At Point of Care [Срок оценки: During or immediately following patient biopsy (same day)]
- Specificity of Machine Learning Analysis of Two Photon Fluorescence Microscopy Images At Point of Care [Срок оценки: During or immediately following patient biopsy (same day)]
Вторичные конечные точки (2)
- Proportion of Discordant Diagnoses Attributable to Machine Learning Model Interpretation Errors [Срок оценки: After completion of patient diagnosis (typically 1-2 weeks after procedure)]
- Proportion of Biopsy Specimens With a Definitive Machine Learning Diagnosis [Срок оценки: During or immediately following patient biopsy (same day)]
Критерии участия
Критерии включения
- Punch, excisional or shave biopsy specimen
Критерии исключения
- 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
Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.
Здоровые добровольцы: Нет
Дизайн исследования
- Распределение
- Не применимо
- Модель
- Одна группа
- Маскирование
- Открытое
- Основная цель
- Диагностика
Центры проведения
США · 1 центр
- Rochester Dermatologic Surgery — Victor
Идентификаторы
NCT: NCT07682831 · STUDY00009823B · R37CA258376