MRImmuno-platform for a rapid diagnostic and therapeutic decision in patients with an outbreak of a laceration of the central nervous system (Q84117): Difference between revisions

From EU Knowledge Graph
Jump to navigation Jump to search
(‎Changed an Item: Fixing rounding issue)
(‎Removed claim: summary (P836): At present, there is no specific parameter to place a number of Multiple Sclerosis (MS). despite diagnostic criteria based primarily on the magnetic resonance imaging (RM), it is necessary to continue to exclude other diseases that mimic the clinical picture SM.So far, a tool has not been developed for a precise SM diagnosis, diagnosis is largely based on the experience of doctors and not on quantifiable indicators, and many patients need long...)
Property / summary
At present, there is no specific parameter to place a number of Multiple Sclerosis (MS). despite diagnostic criteria based primarily on the magnetic resonance imaging (RM), it is necessary to continue to exclude other diseases that mimic the clinical picture SM.So far, a tool has not been developed for a precise SM diagnosis, diagnosis is largely based on the experience of doctors and not on quantifiable indicators, and many patients need long observation of the repelling decision.In addition, up to 13 % of the ill with SM diagnosis is not in fact sick with SM.The project aims to carry out studies on the development of diagnostic profiles and deep learning networks (DL) allowing differential diagnosis of SM in AI (AI) modes.The system will collect data from the RM, optical coherent tomography (retinal) tomography (OCT), immunology and neurodegenerative markers, clinical and neuropsychological data.On the basis of data from 5000 cases (50 % patients with SM), primary standards will be set.The diagnostic pathways set by neurologists and radiologists will include parameters that will be assigned weight for the final diagnosis, the correlation between the parameters will be assessed.The end result will be a set of algorithms and an experimented DL network that will analyse the available collection and include a collection of information in the form of a diagnostic panel, together with an analysis allowing the doctor to clarify the diagnostic decision.For tests, a parameter — sealing, assessing the impact of a parameter on a diagnosis will be attributed to the test.The final value for the patient will be estimated on the basis of all data.An important element will be the automatic detection of RM & OCT images and the introduction of new parameters. (English)
 
Property / summary: At present, there is no specific parameter to place a number of Multiple Sclerosis (MS). despite diagnostic criteria based primarily on the magnetic resonance imaging (RM), it is necessary to continue to exclude other diseases that mimic the clinical picture SM.So far, a tool has not been developed for a precise SM diagnosis, diagnosis is largely based on the experience of doctors and not on quantifiable indicators, and many patients need long observation of the repelling decision.In addition, up to 13 % of the ill with SM diagnosis is not in fact sick with SM.The project aims to carry out studies on the development of diagnostic profiles and deep learning networks (DL) allowing differential diagnosis of SM in AI (AI) modes.The system will collect data from the RM, optical coherent tomography (retinal) tomography (OCT), immunology and neurodegenerative markers, clinical and neuropsychological data.On the basis of data from 5000 cases (50 % patients with SM), primary standards will be set.The diagnostic pathways set by neurologists and radiologists will include parameters that will be assigned weight for the final diagnosis, the correlation between the parameters will be assessed.The end result will be a set of algorithms and an experimented DL network that will analyse the available collection and include a collection of information in the form of a diagnostic panel, together with an analysis allowing the doctor to clarify the diagnostic decision.For tests, a parameter — sealing, assessing the impact of a parameter on a diagnosis will be attributed to the test.The final value for the patient will be estimated on the basis of all data.An important element will be the automatic detection of RM & OCT images and the introduction of new parameters. (English) / rank
Normal rank
 

Revision as of 14:30, 14 October 2020

Project in Poland financed by DG Regio
Language Label Description Also known as
English
MRImmuno-platform for a rapid diagnostic and therapeutic decision in patients with an outbreak of a laceration of the central nervous system
Project in Poland financed by DG Regio

    Statements

    country
    Poland
    0 references
    EU contribution
    8,230,750.45 zloty
    0 references
    1,975,380.11 Euro
    exchange rate to Euro
    0.24 Euro
    point in time
    13 January 2020
    0 references
    budget
    9,221,829.96 zloty
    0 references
    2,213,239.19 Euro
    exchange rate to Euro
    0.24 Euro
    point in time
    13 January 2020
    0 references
    co-financing rate
    89.25 percent
    0 references
    start time
    1 July 2018
    0 references
    end time
    30 June 2021
    0 references
    beneficiary name (string)
    PIXEL TECHNOLOGY SPÓŁKA Z OGRANICZONĄ ODPOWIEDZIALNOŚCIĄ
    0 references
    beneficiary
    Q2510154
    0 references
    summary
    Obecnie nie ma specyficznego parametru pozwalającego na postawienie pewnego rozpoznania stwardnienia rozsianego (SM).Mimo kryteriów diagnostycznych, opartych głównie na obrazowaniu rezonansu magnetycznego (RM),konieczne jest nadal wykluczenie innych chorób naśladujących obraz kliniczny SM. Dotąd nie opracowano narzędzia pozwalającego na precyzyjną diagnozę SM, diagnoza oparta jest w dużej mierze na doświadczeniu lekarzy, a nie kwantyfikowalnych wskaźnikach, a u wielu pacjentów konieczna jest długa obserwacja odwlekająca decyzję terapeutyczną. Ponadto, do 13% chorych z rozpoznaniem SM nie jest w rzeczywistości chorych na SM. Projekt ma na celu przeprowadzenie badań opracowujących profili diagnostycznych i sieci deep learning (DL), umożliwiających diagnostykę różnicową SM w trybie AI (sztucznej inteligencji). W systemie gromadzone będą dane z RM, optycznej koherentnej tomografii siatkówki (OCT), badań immunologicznych i markerów neurodegeneracji, dane kliniczne i neuropsychologiczne. Na podstawie danych z 5000 przypadków (50 % - pacjenci z SM) wytyczone zostaną pierwotne wzorce. Ścieżki diagnostyczne wytyczone przez neurologów i radiologów obejmą parametry, którym przypisana zostanie waga wpływająca na końcową diagnozę, oceniona zostanie korelacja między parametrami. Efektem końcowym będzie zbiór algorytmów i wyćwiczona sieć DL, która będzie analizować dostępne zbiory i prezentować w postaci panelu diagnostycznego zbiór informacji wraz z analizą pozwalającą lekarzowi na precyzyjniejszą decyzję diagnostyczną. Dla badań przypisany będzie parametr - scoring, oceniający wpływ parametru na diagnozę. Na podstawie wszystkich danych szacowana będzie wartość ostateczna dla pacjenta. Ważnym elementem będzie automatyczne wykrywanie nieprawidłowości obrazu RM i OCT oraz wprowadzenie nowych parametrów (Polish)
    0 references

    Identifiers

    Polish Kohesio ID
    POIR.04.01.04-00-0118/17
    0 references
     
    Retrieved from "https://linkedopendata.eu/w/index.php?title=Item:Q84117&oldid=6347608"