View clinical trials related to Parathyroid Diseases.
Filter by:Iatrogenic injuries to the parathyroid glands during thyroid surgery or to the recurrent laryngeal nerve (RLN) do still occur, requiring often specialized management. Recently, it has been demonstrated that the parathyroid gland shows a significant autofluorescence. Using a commercially available Near-InfraRed (NIR) camera (Fluobeam®, Fluoptics©, France), the parathyroid glands can be clearly visualized by contrast-free fluorescence imaging. However it lacks real-time quantification of the fluorescence intensity. The hyperspectral imaging (HSI), which is a technology that combines a spectrometer to a camera system, examines the optical properties of a large area in a wavelength range from NIR to visual light (VIS). It provides spatial information real time, in a contact-free, non-ionizing manner. The HSI technology would add the spatial information, thus enormously enhancing the intraoperative performance. The aim of the proposed study is to identify the spectral features of the important neck target structures, in particular the parathyroid glands, using an appropriate deep learning algorithm, to perform an automated parathyroid recognition. Additionally, this study proposes to compare the detection rate of the hyperspectral based parathyroid recognition with the already existing NIR autofluorescence based recognition.
The intraoperative recognition of target structures, which need to be preserved or selectively removed, is of paramount importance during surgical procedures. This task relies mainly on the anatomical knowledge and experience of the operator. Misperception of the anatomy can have devastating consequences. Hyperspectral imaging (HSI) represents a promising technology that is able to perform a real-time optical scanning over a large area, providing both spatial and spectral information. HSI is an already established method of objectively classifying image information in a number of scientific fields (e.g. remote sensing). Our group recently employed HSI as intraoperative tool in the porcine model to quantify perfusion of the organs of the gastrointestinal tract against robust biological markers. Results showed that this technology is able to quantify bowel blood supply with a high degree of precision. Hyperspectral signatures have been successfully used, coupled to machine learning algorithms, to discriminate fine anatomical structures such as nerves or ureters intraoperatively (unpublished data). The i-EX-MACHYNA3 study aims at translating the HSI technology in combination with several deep learning algorithms to differentiate among different classes of human tissues (including key anatomical structures such as BD, nerves and ureters).
Objectives: 1. To better define the differences in molecular genetics of parathyroid tumors in patients with MEN1, single gland parathyroid disease in patients less than 50 years old and single gland disease in patients greater than 50 years old. 2. To better define the incidence of HRPT2 mutation in young patients with primary hyperparathyroidism and determine whether routine testing in these patients is indicated.
The purpose of this study is to design a method to better localize parathyroid adenomas. This study will include approximately 6 patients who have not had surgery and another 25 patients who have already had surgery over the course of one year.