The scope of ArtIPred project is to design and validate a smart health system based on artificial intelligence (AI) as a predictor for chronic kidney disease development using ECG signals from animal models. The main advantage of this model is the fact that it will provide a safe non-invasive way for patients to determine the state of their kidneys. The proposed architecture implementing the ArtiPred system. With the use of a wireless sensor we can capture ECG signals that will be further processed amongst with other clinical data acquired with medical equipment. The goal is to establish a clinical framework which will be the basis of the CKD models development. In order to capture the clinical data we will deploy a web interface which will allow to register and store the clinical observation of the medical personnel, based on imagistics and biochemistry trials. After establishing the clinical framework and achieve the CKD data models, we will study and experiment specific AI solutions in order to identify the correlations between the ECG data sets and the disease evolution. The main goal is to develop and validate, at laboratory level, an artificial intelligence tool which will allow early diagnosis of the CKD.
- Phase I-2020: Done
WP1. Design of conceptual models for CKD assessment and ECG signal processing analysis
- Phase II-2021: Done
WP2. In vivo evaluation of CKD models and ECG signal processing analysis
- Phase III-2022: Done
WP3. Chronic kidney disease models evaluation and data analysis
Phase 1: Short report 2020
Phase II: Short report 2021
Phase III: Short report 2022
- Oral presentation at ICEMS-BIOMED 2022– International Conference on Electromagnetic Fields, Signals and BioMedical Engineering, 19-20 May 2022, Sibiu, Romania.
- Poster presentation (ID 250) at Congresul Universității de Medicină și Farmacie „Carol Davila” București, 25-27 November 2021, București, Romania.
- Poster presentation at IEEE International Conference on e-Health and Bioengineering EHB 2021– 9th Edition, 18-19 November 2021, Iasi, Romania.
- Expending the power of artificial intelligence in preclinical research: an overview by A Diaconu, F D Cojocaru, I Gardikiotis, L Agrigoroaie, D M Furcea, A Pasat, G Suciu, C Rezuș and G Dodi, in IOP Conference Series: Materials Science and Engineering.
- In preparation