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Biomedical Engineering Research

At C-BER our main goals are the creation of interdisciplinary knowledge enabling innovation and technology transfer with economic impact; and also the development of products, tools and methods for the prevention and early detection of different types of diseases, aging-related impairments, or for human rehabilitation, physical therapy or functional assessment.

We also seek to contribute to the development of advanced neuro-technologies at the frontier of engineering and neurology, and to promote strategic partnerships with clinical partners, research institutes, and fostering international cooperation.

Our R&D activity is developed in three different areas: BioInstrumentation, Biomedical Imaging and NeuroEngineering.

Latest News
Networked Intelligent Systems

INESC TEC Spin-off awarded €100K to expand clinical trials in patients with Parkinson's disease

The spin-off inSignals Neurotech, dedicated to the development of medical devices for the quantification of motor symptoms of neurodegenerative diseases, in order to obtain better clinical results, received €100K through the INNOV-ID funding call - an initiative promoted by Portugal Ventures, focusing on the measures established by the Government to capitalise Portuguese companies during the pandemic.

26th March 2021

Networked Intelligent Systems

Unprecedented study sugests improvements in the diagnosis and monitoring in patients with familial amyloid polyneuropathy

In the scientific article “Clinical 3-D Gait Assessment of Patients With Polyneuropathy Associated With Hereditary Transthyretin Amyloidosis”, published in Frontiers in Neurology, one of the most important journals in the area, the researchers analysed the gait parameters of different groups of patients with familial amyloid polyneuropathy, according to different stages of development.

18th December 2020

Networked Intelligent Systems

INESC TEC developed a tool for the automatic analysis of X-rays images, in order to assess the evolution of COVID-19 patients

A team of researchers from INESC TEC’s Centre for Biomedical Engineering Research (C-BER) developed a computer-assisted diagnosis system, in partnership with radiologists from the Vila Nova de Gaia/Espinho hospital centre (CHVNGE) and the Northern Region Health Administration (ARS Norte).

14th December 2020

Networked Intelligent Systems

INESC TEC technology in the first neurostimulator in the world using long-term monitoring in epileptic patient

The new neurostimulator produced by the North-American company Medtronic relies on technology developed by INESC TEC’s Centre for Biomedical Engineering Research (C-BER), in order to measure the movements caused by epileptic events in 3D, and connect them with deep brain stimulation, as well as brain surface signals (EEG). This device was approved for human use in Europe in January this year, and it enables stimulating and capturing brain signals simultaneously, which can make all the difference in the patients’ quality of life. For the first time in the world, this neurostimulator was implanted in an epileptic patient, and used in a study on 3D EEG video technology in late July, at the University Hospital Centre of São João (CHUSJ).

14th September 2020

Networked Intelligent Systems

INESC TEC spin-off acknowledged as one of the most promising in the world in digital health

ILoF – Intelligent on Fiber, a spin-off from the University of Porto, established in INESC TEC and currently incubated at the Faculty of Medicine (FMUP), is among the 150 most promising start-ups in the field of digital health worldwide, according to a list provided by CB Insights, a North American company specialised in business analysis.

31st August 2020

Interest Topics
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Featured Projects

CAGED

Computer Assisted Gastric Cancer Diagnosis

2021-2024

THOR

THOR - Computer Assisted Thoracic Assessment using POCUS

2021-2024

C4MiR_BIP_Proof

C4MiR_BIP_Proof_PROGRAMA DE ATRIBUIÇÃO DE APOIOS PARA PROVA DE CONCEITO DA UNIVERSIDADE DO PORTO

2020-2021

iiLab

Ampliação da Infraestrutura Tecnológica do INESC TEC para a Transformação Digital da Indústria

2020-2022

VitalPROVID

VitalPROVID

2020-2021

CAIRUS

COVID-19 Artificial Intelligence-based Risk Unified Stratification tool for clinical management

2020-2020

CXR_AI4COVID19

Chest Radiography-based AI for Supporting ClinicalDecision on Covid-19

2020-2020

TAMI

Transparent Artificial Medical Intelligence

2020-2023

BioNanoTech

Serviços de apoio técnico - iLoF spin-off

2020-2021

MRI_Simulator

Aluguer do Simulador de Ressonância Magnética à Faculdade de Psicologia e de Ciências da Educação da Universidade do Porto

2019-2019

WalkingPAD

Patient education on a quantified supervised home-based exercise therapy to improve walking ability in patients with peripheral arterial disease and intermittent claudication

2019-2021

Serv_Neuro

Consultoria Insignals Neurotech no âmbito do programa EIT Health Startups Meets Pharma 2019

2019-2019

LUCAS

Lung cancer screening - A non-invasive methodology for early diagnosis

2018-2022

PERFECT

Perceptual equivalence in virtual reality for authentic training

2018-2021

TexBoost

Less Commodities more Specialities

2017-2020

LNDetector

Automatic Detection, Segmentation and Classification of Pulmonary Nodules System in Computed Tomography Images

2016-2019

SCREEN-DR

Image Analysis and Machine Learning Platform for Innovation in Diabetic Retinopathy Screening

2016-2020

Bio-Early

Projeto Vital Sticker no âmbito do Contrato Programa

2015-2018

NanoStima-RL5

NanoSTIMA - Advanced Methodologies for Computer-Aided Detection and Diagnosis

2015-2019

NanoStima-RL1

NanoSTIMA - Macro-to-Nano Human Sensing Technologies

2015-2019

SMILES

SMILES - Smart, Mobile, Intelligent and Large scale Sensing and analytics

2015-2019

VR2Market

VR2Market: Towards a Mobile Wearable Health Surveillance Product for First Response and other Hazardous Professions

2014-2019

STePMotion

Spatio-temporal components of the processing of sensorial and motor information

2014-2015

EcoDrive

Inteligent Eco Driving and Fleet Management

2014-2015

Re-Learning

Human motor re-learning by sensor information fusion

2014-2015

VitalResponder2

Intelligent management of critical events of stress, fatigue and smoke intoxication in forest firefighting

2013-2015

ASD-MD

Movement Disorders in Autistic Spectrum Disorders

2013-2015

HERMES

Interactive system for digital content consumers

2013-2015

Team
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Laboratories

Biomedical Imaging Laboratory

Neuroengineering and Advanced Human Sensing Laboratory

BioInstrumentation Lab

Publications

C-BER Publications

View all Publications

2021

Multivariate Outlier Detection in Postprocessing of Multi-temporal PS-InSAR Results using Deep Learning

Authors
Aguiar, P; Cunha, A; Bakon, M; Ruiz-Armenteros, AM; Sousa, JJ;

Publication
Procedia Computer Science

Abstract

2021

Comprehensive Perspective for Lung Cancer Characterisation Based on AI Solutions Using CT Images

Authors
Pereira, T; Freitas, C; Costa, JL; Morgado, J; Silva, F; Negrao, E; de Lima, BF; da Silva, MC; Madureira, AJ; Ramos, I; Hespanhol, V; Cunha, A; Oliveira, HP;

Publication
Journal of Clinical Medicine

Abstract
Lung cancer is still the leading cause of cancer death in the world. For this reason, novel approaches for early and more accurate diagnosis are needed. Computer-aided decision (CAD) can be an interesting option for a noninvasive tumour characterisation based on thoracic computed tomography (CT) image analysis. Until now, radiomics have been focused on tumour features analysis, and have not considered the information on other lung structures that can have relevant features for tumour genotype classification, especially for epidermal growth factor receptor (EGFR), which is the mutation with the most successful targeted therapies. With this perspective paper, we aim to explore a comprehensive analysis of the need to combine the information from tumours with other lung structures for the next generation of CADs, which could create a high impact on targeted therapies and personalised medicine. The forthcoming artificial intelligence (AI)-based approaches for lung cancer assessment should be able to make a holistic analysis, capturing information from pathological processes involved in cancer development. The powerful and interpretable AI models allow us to identify novel biomarkers of cancer development, contributing to new insights about the pathological processes, and making a more accurate diagnosis to help in the treatment plan selection.

2021

LNDb Challenge on automatic lung cancer patient management

Authors
Pedrosa, J; Aresta, G; Ferreira, C; Atwal, G; Phoulady, HA; Chen, X; Chen, R; Li, J; Wang, L; Galdran, A; Bouchachia, H; Kaluva, KC; Vaidhya, K; Chunduru, A; Tarai, S; Nadimpalli, SPP; Vaidya, S; Kim, I; Rassadin, A; Tian, Z; Sun, Z; Jia, Y; Men, X; Ramos, I; Cunha, A; Campilho, A;

Publication
Medical Image Analysis

Abstract

2021

Machine Learning and Feature Selection Methods for EGFR Mutation Status Prediction in Lung Cancer

Authors
Morgado, J; Pereira, T; Silva, F; Freitas, C; Negrão, E; de Lima, BF; da Silva, MC; Madureira, AJ; Ramos, I; Hespanhol, V; Costa, JL; Cunha, A; Oliveira, HP;

Publication
Applied Sciences

Abstract
The evolution of personalized medicine has changed the therapeutic strategy from classical chemotherapy and radiotherapy to a genetic modification targeted therapy, and although biopsy is the traditional method to genetically characterize lung cancer tumor, it is an invasive and painful procedure for the patient. Nodule image features extracted from computed tomography (CT) scans have been used to create machine learning models that predict gene mutation status in a noninvasive, fast, and easy-to-use manner. However, recent studies have shown that radiomic features extracted from an extended region of interest (ROI) beyond the tumor, might be more relevant to predict the mutation status in lung cancer, and consequently may be used to significantly decrease the mortality rate of patients battling this condition. In this work, we investigated the relation between image phenotypes and the mutation status of Epidermal Growth Factor Receptor (EGFR), the most frequently mutated gene in lung cancer with several approved targeted-therapies, using radiomic features extracted from the lung containing the nodule. A variety of linear, nonlinear, and ensemble predictive classification models, along with several feature selection methods, were used to classify the binary outcome of wild-type or mutant EGFR mutation status. The results show that a comprehensive approach using a ROI that included the lung with nodule can capture relevant information and successfully predict the EGFR mutation status with increased performance compared to local nodule analyses. Linear Support Vector Machine, Elastic Net, and Logistic Regression, combined with the Principal Component Analysis feature selection method implemented with 70% of variance in the feature set, were the best-performing classifiers, reaching Area Under the Curve (AUC) values ranging from 0.725 to 0.737. This approach that exploits a holistic analysis indicates that information from more extensive regions of the lung containing the nodule allows a more complete lung cancer characterization and should be considered in future radiogenomic studies.

2021

Explainability Metrics of Deep Convolutional Networks for Photoplethysmography Quality Assessment

Authors
Zhang, O; Ding, C; Pereira, T; Xiao, R; Gadhoumi, K; Meisel, K; Lee, RJ; Chen, YR; Hu, X;

Publication
IEEE Access

Abstract

Facts & Figures

16Papers in indexed journals

2020

0Book Chapters

2020

1R&D Employees

2020

Contacts