DEVELOPMENT OF A CLINICAL IMAGE ANALYSIS MODEL AIMING TO SUPPORT MEDICAL DIAGNOSIS DURING COVID-19 PANDEMIC (Q4225702)
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Project Q4225702 in Italy
| Language | Label | Description | Also known as |
|---|---|---|---|
| English | DEVELOPMENT OF A CLINICAL IMAGE ANALYSIS MODEL AIMING TO SUPPORT MEDICAL DIAGNOSIS DURING COVID-19 PANDEMIC |
Project Q4225702 in Italy |
Statements
230,013.04 Euro
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460,026.09 Euro
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50.0 percent
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MIKAMAI SRL
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LOOPTRIBE S.R.L.
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CENTRO DI RICERCA, SVILUPPO E STUDI SUPERIORI IN SARDEGNA SOCIETA' A RESPONSABILITA' LIMITATA ED IN FORMA ABBREVIATA CRS4 S.R.L.
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ISTITUTO AUXOLOGICO ITALIANO
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ASST FATEBENEFRATELLI SACCO
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IL PROGETTO HA COME OBIETTIVO DI PREDISPORRE UN SISTEMA DI STUDIO BASATO SULL INTELLIGENZA ARTIFICIALE (AI) IN GRADO DI ANALIZZARE LE IMMAGINI ECOCARDIOGRAFICHE, LE ECOGRAFIE POLMONARI E LE IMMAGINI RADIOGRAFICHE POLMONARI DI PAZIENTI MAGGIORENNI (COVID-19 NEGATIVI, COVID-19 POSITIVI E PAZIENTI CON ARDS-COVID-19 RICOVERATI NEI REPARTI DEGLI OSPEDALI DEL PARTENARIATO), UNITAMENTE ALLE INFORMAZIONI DI REFERTO, COSì DA COSTRUIRE UN DATASET UTILE ALL ADDESTRAMENTO DEL MODELLO DI MACHINE LEARNING FINO A SELEZIONARE IL MODELLO IN GRADO DI FORNIRE LA PREVISIONE PIù ACCURATA DI DIAGNOSI. TALE MODELLO PRODURRà UNA CLASSIFICAZIONE BINARIA DELLA PROBABILITà DI DIAGNOSI DI NON-PATOLOGIA DA COVID-19 OPPURE DI PATOLOGIA DA COVID-19 PER SUPPORTARE I CLINICI NELLA DIAGNOSI - IN TERMINI DI PRECOCITà , MINORE DIFFICOLTà NELLA DIAGNOSI DIFFERENZIALE,STRATIFICAZIONE DEL RISCHIO E INIZIO TEMPESTIVO DELLA TERAPIA OTTIMALE- E NEL FOLLOW UP (IN TERMINI DI PREDITTORI PRECOCI DI DECORSO CLINICO SFAVOREVO (Italian)
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THE PROJECT AIMS TO SET UP AN ARTIFICIAL INTELLIGENCE-BASED STUDY SYSTEM (AI) ABLE TO ANALYSE ECHOCARDIOGRAPHIC IMAGES, PULMONARY ULTRASOUND AND X-RAY IMAGES OF ADULT PATIENTS (COVID-19 NEGATIVE, COVID-19 POSITIVE AND ARDS-COVID-19 PATIENTS HOSPITALISED IN THE PARTNERSHIP HOSPITALS), TOGETHER WITH THE REPORTING INFORMATION, SO AS TO BUILD A DATASET USEFUL FOR THE TRAINING OF THE MACHINE LEARNING MODEL UNTIL SELECTING THE MODEL CAPABLE OF PROVIDING THE MOST ACCURATE PREDICTION OF DIAGNOSIS. THIS MODEL WILL PRODUCE A BINARY CLASSIFICATION OF THE PROBABILITY OF DIAGNOSIS OF NON-PATHOLOGY FROM COVID-19 OR COVID-19 PATHOLOGY TO SUPPORT CLINICIANS IN DIAGNOSIS — IN TERMS OF EARLYNESS, LESS DIFFICULTY IN DIFFERENTIAL DIAGNOSIS, RISK STRATIFICATION AND EARLY INITIATION OF OTTIMAL THERAPY- AND FOLLOW-UP (IN TERMS OF EARLY PREDICTORS OF ADVERSE CLINICAL COURSE (English)
1 February 2022
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LE PROJET VISE À METTRE EN PLACE UN SYSTÈME D’ÉTUDE BASÉ SUR L’INTELLIGENCE ARTIFICIELLE (IA) CAPABLE D’ANALYSER DES IMAGES ÉCHOCARDIOGRAPHIQUES, DES ÉCHOGRAPHIES PULMONAIRES ET DES IMAGES RADIOGRAPHIQUES DE PATIENTS ADULTES (COVID-19 NÉGATIF, COVID-19 POSITIF ET ARDS-COVID-19 HOSPITALISÉS DANS LES HÔPITAUX PARTENAIRES), AINSI QUE LES INFORMATIONS DE DÉCLARATION, DE MANIÈRE À CONSTRUIRE UN ENSEMBLE DE DONNÉES UTILE POUR LA FORMATION DU MODÈLE D’APPRENTISSAGE AUTOMATIQUE JUSQU’À LA SÉLECTION DU MODÈLE CAPABLE DE FOURNIR LA PRÉDICTION LA PLUS PRÉCISE DU DIAGNOSTIC. CE MODÈLE PRODUIRA UNE CLASSIFICATION BINAIRE DE LA PROBABILITÉ DE DIAGNOSTIC DE NON-PATHOLOGIE À PARTIR DE LA PATHOLOGIE COVID-19 OU COVID-19 AFIN DE SOUTENIR LES CLINICIENS DANS LE DIAGNOSTIC — EN TERMES DE PRÉCOCE, MOINS DE DIFFICULTÉ DANS LE DIAGNOSTIC DIFFÉRENTIEL, STRATIFICATION DES RISQUES ET DÉBUT PRÉCOCE DE LA TÉRAPIE OTTIMALE- ET SUIVI (EN TERMES DE PRÉDICTEURS PRÉCOCES DE COURS CLINIQUES INDÉSIRABLES (French)
2 February 2022
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MILANO
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BRESCIA
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