Center of Science, Technology and Development for innovation in Medicine and Health: inLab.iNova

• Grant number: 21/11905-0
• Support Opportunities: Research Grants - Science Centers for Development
• Start date: June 01, 2022
• End date: May 31, 2027
• Field of knowledge: Health Sciences - Medicine
• Principal Investigator: Giovanni Guido Cerri
• Grantee: Giovanni Guido Cerri
• Host Institution: Faculdade de Medicina (FM). Universidade de São Paulo (USP). São Paulo , SP, Brazil
• Pesquisadores principais: André Carlos Ponce de Leon Ferreira de Carvalho ; Claudia da Costa Leite ; Eduardo Mario Dias ; Leandro Nunes de Castro Silva ; Marcelo Tatit Sapienza
• Associated researchers: Adriano Galindo Leal ; André Kazuo Takahata ; Antonio Valerio Netto ; Cristina Pires Camargo ; Jorge Futoshi Yamamoto ; Lilian Berton ; Moacyr Martucci Jr ; Ricardo Suyama ; Rodrigo Bonacin ; Umberto Tachinardi
• Associated scholarship(s): 25/04494-5 - Artificial intelligence for brain health assessment using magnetic resonance imaging., BP.IC; 24/22139-5 - Retrospective Analysis of Imaging Data Using Artificial Intelligence Methods for Diagnostic Assessment of Amyloid PET in Alzheimer's Disease, BP.MS; 24/18658-7 - Application of Computational Methods and Techniques for the Development of an Application for the Management of Chronic Diseases: DIABETES MELLITUS (DM), BP.IC; + associated scholarships 24/19593-6 - Application of computational methods and techniques for developing an application for controlling chronic diseases: HIGH BLOOD PRESSURE, BP.IC; 24/18602-1 - Research project about integrated information systems in healthcare, including big data bases, big data, Artificial inteligence, predictive models, advanced diagnostics tools, prevention and treatmente in medicine., BP.IC; 24/18603-8 - Application of Computational Methods and Techniques for the Development of an Application for Chronic Disease Management: CHRONIC BRONCHITIS, BP.IC; 24/18604-4 - Application of computational methods and techniques for the development of an application for the management of chronic diseases: chronic kidney disease (CKD), BP.IC; 24/18605-0 - Application of computational methods and techniques for the development of an application for the control of chronic diseases: BRONCHOPULMONARY DYSPLASIA, BP.IC; 24/18657-0 - Application of Computational Methods and Techniques for the Development of an App for Chronic Disease Management: Chronic Obstructive Pulmonary Disease (COPD), BP.IC; 24/18948-5 - Application of computational methods and techniques for the development of an app for chronic disease management: OBESITY, BP.IC; 24/18924-9 - Application of computacional methods and techniques for the development of an app for the control of chronic diseases: CHRONIC NEUROLOGICAL DISEASES, BP.IC; 24/14707-3 - Development of an evaluation methodology based on criteria for the successful implementation of AI systems in Radiology with 'lato sensu' quality., BP.DR; 24/03429-2 - TRANSFER OF LEARNING TO RECOGNITION OF MEDICAL IMAGES RELATED TO CANCER., BP.DR; 24/08815-8 - AI in Medicine: Brazilian Residents' Perspectives by Program, Gender and Tech Literacy, BP.IC; 23/13355-3 - Research Project on integrated health information systems, including large databases, big data, artificial intelligence, predictive models, advanced technologies for diagnosis, prevention and treatments in Medicine, BP.TT; 23/10867-3 - Noise detection in 7T functional magnetic resonance imaging using artificial intelligence, BP.PD - associated scholarships

Abstract

This document deals with the Research Project on integrated health information systems, including large databases (big data), artificial intelligence, predictive models, advanced therapies for diagnosis, prevention and treatment of chronic diseases to be carried out by Inova HC, Nucleus of Technological Innovation, Hospital das Clínicas, Faculty of Medicine, USP. The area of Medicine and Health is an essential scientific field for the development of society and the evolution of humanity. The recent worldwide pandemic demonstrated how fundamental and necessary it is to develop new methods and techniques for diagnosis and therapy that allow quick and effective solutions to health problems. The application of Artificial Intelligence (AI) techniques in Medicine and Health has the potential to improve, speed up and automate the diagnosis and treatment of some of the main diseases that afflict humanity. In this respect, this innovation will allow a scientific and technological leap that could benefit thousands of people and patients. Objectives: Conduct basic and applied problem-oriented research, seeking to produce wealth for Brazil and contribute to guaranteeing the rights and quality of life of Brazilians; Carry out projects in partnership with governmental and non-governmental bodies; Generate Startups or Spin-offs that incorporate research results developed by the Center in its products or services; Substantially contribute to the training of qualified labor at the technical, technologist, bachelor's and lato and/or stricto sensu levels in the area of Artificial Intelligence applied in the thematic areas of Medicine and Health; Focus on common advanced scientific and technological research, articulating the research activities to be developed, which is the field of Artificial Intelligence in Medicine and Health. In this project we aim to implement a Research Center for Development (CCD) on integrated information systems in health, including large databases (big data), artificial intelligence, predictive models, advanced therapies for diagnosis, prevention and treatment of chronic diseases in the health area. The project is carried out jointly by the Faculty of Medicine of USP (FMUSP), the Hospital das Clínicas at FMUSP (HC), the Institute of Radiology at the HC (InRad), the Polytechnic School of Engineering at USP (POLI). This partnership aims to implement platforms, develop methods and create new algorithms to support diagnosis and therapy in Medicine and Health Management. HCFMUSP serves approximately 1.5 million patients per year. It is the largest medical complex in South America. It is linked to the Health Department of the State of São Paulo, responsible for a population of 44 million people, for care purposes. It is linked to and to FMUSP for teaching and research purposes. FMUSP has 1,000 undergraduate and 2,000 graduate students. FMUSP's stricto sensu Post-graduation is made up of 27 programs at the Academic Master's, Professional Master's and Doctoral levels, aimed at training researchers and professors in higher education. It has approximately 500 accredited advisors and plays an important role in the country's scientific production, with publications in high-impact journals at national and international level. The Polytechnic School of Engineering at USP has a number of undergraduate and graduate students equivalent to the Faculty of Medicine. Undergraduate and graduate courses are planned, at both faculties, at USP. This platform has the potential to be used in units of the SUS Unified Health System. First, we intend to offer the SES-SP Health Secretariat of the State of São Paulo, and for all federative units. (AU)

Scientific publications

(7)

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

LIMA, BRUNO C. S.; FRANGO, ISMAR S.; DE CASTRO, LEANDRO N.. An Ant Colony Optimization Approach to Optimize Biblical Sermons. DISTRIBUTED COMPUTING AND ARTIFICIAL INTELLIGENCE, 21ST INTERNATIONAL CONFERENCE, v. 1259, p. 10-pg., 2025-01-01. (21/11905-0)

XAVIER, RITA; PELLER, JOHN; DE CASTRO, LEANDRO N.. TRUNC: A Transfer Learning Unsupervised Network for Data Clustering. DISTRIBUTED COMPUTING AND ARTIFICIAL INTELLIGENCE, 21ST INTERNATIONAL CONFERENCE, v. 1259, p. 11-pg., 2025-01-01. (21/11905-0)

FRANCA, MILTON; AMARAL, ANGELO; ROSA, FERRUCIO DE FRANCO; BONACIN, RODRIGO. Scientific Knowledge Database to Support Cybersickness Detection and Prevention. VIRTUAL, AUGMENTED AND MIXED REALITY, PT I, VAMR 2024, v. 14706, p. 18-pg., 2024-01-01. (21/11905-0)

SANTOS, MATHEUS A. DE CASTRO; BERTON, LILIAN. An Enhanced Framework for Overcoming Pitfalls and Enabling Model Interpretation in Pneumonia and Covid-19 Classification. IEEE ACCESS, v. 11, p. 18-pg., 2023-01-01. (21/14725-3, 21/11905-0)

XAVIER, RITA; PELLER, JOHN; DE CASTRO, LEANDRO NUNES. TRUNC: A Transfer Learning Unsupervised Network for Data Clustering. IEEE ACCESS, v. 13, p. 17-pg., 2025-01-01. (21/11905-0)

FERRARIA, MATHEUS A.; BALBI, PEDRO P.; DE CASTRO, LEANDRO N.. An Analysis of Different Text Representation Schemes for an Immune Clustering Algorithm. DISTRIBUTED COMPUTING AND ARTIFICIAL INTELLIGENCE, 21ST INTERNATIONAL CONFERENCE, v. 1259, p. 11-pg., 2025-01-01. (21/11905-0)

BONACIN, RODRIGO; DE FIGUEIREDO, ELAINE BARBOSA; ROSA, FERRUCIO DE FRANCO; DOS REIS, JULIO CESAR; DAMETTO, MARIANGELA. The reuse of electronic health records information models in the oncology domain: Studies with the bioframe framework. JOURNAL OF BIOMEDICAL INFORMATICS, v. 157, p. 9-pg., 2024-08-17. (21/11905-0)