Featured conditions Brain tumor, breast cancer, colon cancer, congenital heart disease, heart arrhythmia. See more conditions.
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(for at least one location)
Rochester, MN
Describes the nature of a clinical study. Types include:
The purpose of this study, as a proof-of-concept, is to investigate whether ex vivo fluorescence microscopy can provide adequate visualization of cutaneous tissue for determination of non-melanoma skin cancer tumor presence in a sample of up to 250 residual skin specimens.
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Open for enrollment
The study aims are to develop a consent-based IRB proposal that will enroll newborns who have imperforate anus. The proposal will include permission for chart review and database information storage, to develop a database that will include MRN, imperforate anus phenotype, information on other congenital malformations, syndromic diagnosis if available, demographic information, and to develop a biobank of DNA and white blood cell samples from infants with imperforate anus. If patients are undergoing a skin biopsy for a medically indicated reason, cells will be requested.
Imperforate Anus, also known as anal atresia, is a rare birth defect. Unable to pass stool through the gastrointestinal tract, this condition can result in death of the newborn and emergency surgery is required once discovered. More than two thirds of affected infants have other birth defects that include other parts of the gastrointestinal tract, airway, heart, skeleton, kidneys, eyes, or ears. The exact prevalence of imperforate anus in the newborn population is unknown.
The purpose of this study is to validate the use of ex-vivo confocal microscopy for the assessment of skin specimens derived from Mohs surgery compared to standard microscopy. To leverage deep learning models for classification, detection, and localization of tumor for extramammary Paget’s disease on ex-vivo confocal microscopy images. To leverage AI/ML to ex-vivo confocal microscopy imaging to facilitate microscopic-level imaging of extramammary Paget’s disease. To leverage deep learning models for classification, detection, and localization of tumor for extramammary Paget’s disease on frozen and permanent pathology WSIs.
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