2024 – Ongoing – Quantum Computing for Medical Data Privacy

This project explores the application of quantum computing to healthcare data processing through a collaboration between the Heart Institute (InCor-HCFMUSP) and Dynex, a global developer of Quantum-as-a-Service (QaaS) technology. The initiative aims to investigate how quantum-enhanced computational architectures can support medical workflows, strengthen data security, and increase efficiency in large-scale health data analysis. In its first phase, the project focuses on developing quantum-powered solutions for medical text anonymization—an essential step in protecting patient privacy while enabling the ethical use of electronic health records for research and artificial intelligence applications. By leveraging neuromorphic quantum optimization in combination with advanced language models, the project seeks to establish new methods for secure, scalable, and privacy-preserving data processing in healthcare.

The validation report of the first phase is available at this attached file:

2025 – 2027 (CNPq 444647/2025-2) – Artificial Intelligence Platform for Multimodal Health Data extraction and Integration for SUS Applications

The growing digitalization of healthcare—through electronic records, imaging exams, wearable devices, and omics data—has produced a vast amount of clinical information. However, these data remain fragmented, heterogeneous, and often unstructured, which limits their integration and use for clinical and management decision-making. In Brazil’s Unified Health System (SUS), this fragmentation affects the quality of care, epidemiological surveillance, and resource allocation. Artificial intelligence (AI) technologies, particularly natural language processing (NLP) and multimodal data integration, offer promising tools to transform these records into actionable knowledge while complying with data protection regulations such as the LGPD. The project addresses the current lack of interoperable solutions in Brazil capable of extracting and integrating multimodal clinical data from the SUS while preserving privacy and ensuring secure, efficient use. It proposes the development of an AI-based platform tailored to the Brazilian context, designed to automatically extract relevant information and integrate it with structured data, images, and biomedical signals, providing valuable support for clinical and management decisions..

2014 – 2025 (FAPESP 2014/50889-7) – National Institute of Science & Technology in Medicine Assisted by Scientific Computing (INCT-MACC)

The main objective of this proposal is to transfer innovative medical applications to the Health System, oriented towards diagnosis, treatment, surgical planning, training and computer-aided decision support. The National Institute of Science and Technology in Medicine Assisted by Scientific Computing (INCT-MACC) has been allowing, since 2008, to successfully integrate a network of knowledge in modern communication techniques and multimedia transmission, developing and managing computing environments in cloud and high performance for computer modeling and simulation of physiological systems that make up the human body, involving different scales at the molecular, cellular and systemic levels, promoting the development of medical image processing, scientific visualization and virtual reality in the development of innovative medical applications.

2020 – 2024 (CNPq 307734/2019-6) – Quantification of cardiac structures through machine learning techniques

The aim of this project is to continue the line of research in Modeling and Computational Simulation for Automatic Quantification of Mechanical Properties and Kinetics of Regions of Cardiac Muscle, as part of our activities as head of the Associated Laboratory of the National Institute of Science and Technology (INCT ) in Medicine Assisted by Scientific Computation (MACC) (http://macc.lncc.br/). Therefore, it is intended to validate the proposed methods through mathematical phantoms and dynamic physical phantoms aiming refinements and optimization. It is also intended to evaluate and propose friendly and intuitive ways of presenting information to the clinician to aid in diagnosis.

2020 – 2024 (FAPESP 2020/11258-2) – Queries for similarity and interoperability in medical databases

Health institutions store patient data (Electronic Patient Records – REP) in their own repositories, using the structural particularities defined in each institution to carry out their activities. However, a given individual’s data is often spread across many repositories in different institutions and formats leading to the frequent need to share information between institutions. Given the differences in how and what data are recorded, the need for standards for their interoperability arises, and one of the most recent and promising technologies for sharing medical data is based on the Common Model of Observational Data in Medicine (OMOP-CDM) proposed by OHDSI initiative. The data stored in REP is fundamentally multimodal (images, videos, time series, etc.), acquired from different sources and with varied structures or formats that must be properly managed. This project aims to make fonts and formats compatible, developing techniques to integrate previous GBdI research results obtained with data from medical exams based on images in OMOP. Although there is still no standardized support for similarity comparisons or for complex data types such as images in OMOP, this research intends to develop it in the MIVisDB Project and make it available to the community in the area.

2017 – 2024 (FAPESP 2016/17078-0) – Mining, indexing and visualization of Big Data in the context of clinical decision support systems (MIVisBD)

Today, almost all human activities generate and/or demand to store and process ever-increasing sets of data, which are often diverse and complex. This scenario of vertiginous growth, which occurs both in the scientific, academic, business and even leisure activities, demands new efficient methods for organization and access. Such a scenario is being called as the “age of big data”. Health-related activities and systems are at the center of this scenario, as they produce large amounts of diverse and complex data. It is important that we advance technologically, in order to benefit from this volume of data to expand knowledge of the areas, in order, for example, to support the decision-making process. This decision support in complex systems is increasingly guided by the information extracted and what is learned from these large volumes of data. In a clinical environment, the Electronic Patient Records (ERP) constitute a propitious platform for the development of strategies for extracting information from patients, their profiles and even from groups that have the same casuistry. In this project, we intend to integrate innovative database supports, image processing and visual data analysis methods based on REPs and clinical data repositories to gather valuable and meaningful information for decision making that support diagnosis and treatment. of patients. The size and complexity of REP databases pose major processing challenges, both in terms of the development and application of analysis and knowledge extraction techniques, and in terms of supporting the development of practical tools for clinical use. However, they also incorporate a myriad of opportunities to create algorithms and methods capable of displaying relevant information related to a particular patient or groups of patients, which would usually be hidden by the large volume of data. Furthermore, an efficient manipulation of this data has high potential to make REPs a more effective platform to support healthcare professionals, dealing with fast-demand medical applications as well as strategic government decisions in healthcare. In this project we will develop methods and algorithms that will be materialized in a modular platform to be made available to the community in the area, supporting the daily decision-making in health systems.

2020 – 2023 (FAPESP 2019/25153-0) – Deep Learning Algorithms for Classifying Electrocardiograms

The use of telemedicine systems makes it possible to expand cardiac medical care to populations in poor regions that are further away from urban centers. This is especially important in our country, of continental dimensions and with millions of patients using the public health system. This project aims to develop a tool for analyzing and classifying electrocardiogram (ECG) signals in terms of normality or alteration. The set of algorithms to be developed will be able to extend the classifiers to include categories that also allow the diagnosis of arrhythmias and acute myocardial infarction. As multiple disorders can present concurrently on a single ECG, we will explore multi-label classification approaches. The training of these classifiers will be carried out using 200,000 ECG records and associated reports. In addition, we will take into account the clinical and socio-demographic variables of the individuals, which potentially influence the diagnosis. For feature extraction, we will consider supervised deep learning techniques. Initially, we will explore variants of convolutional neural networks to extract information directly from the images of electrocardiographic tracings. In a second moment, so that temporal information of the signals is taken into account, we will develop a tool to convert the images into digital signals and, then, we will explore variants of recurrent neural networks. Furthermore, we will consider a classification approach based both on manually extracted features of digital signals and on features learned by deep learning techniques.