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About

About

Francesco Renna received the Laurea Specialistica degree in telecommunication engineering and the Ph.D. degree in information engineering, both from the University of Padova, Padova, Italy, in 2006 and 2011, respectively. Between 2007 and 2019, he held Visiting Researcher and Postdoctoral appointments with Infineon Technology AG, Princeton University, Georgia Institute of Technology (Lorraine Campus), Supelec, University of Porto, Duke University, University College London, and University of Cambridge. From 2019 to 2022, he has been an Assistant Researcher at Instituto de Telecomunicações, Porto. Since 2023, he has been an Assistant Professor at the University of Porto, Portugal. Since 2022, he is a researcher with INESC TEC.


?His research interests include high-dimensional information processing and biomedical signal and image processing. Dr. Renna was the recipient of a Marie Sklodowska-Curie Individual Fellowship from the European Commission and a Research Contract within the Scientific Employment Stimulus program from the Portuguese Foundation for Science and Technology.

Interest
Topics
Details

Details

  • Name

    Francesco Renna
  • Since

    01st June 2020
  • Nationality

    Itália
  • Contacts

    +351222094000
    francesco.renna@inesctec.pt
005
Publications

2025

QUAIDE - Quality assessment of AI preclinical studies in diagnostic endoscopy

Authors
Antonelli, G; Libanio, D; De Groof, AJ; van der Sommen, F; Mascagni, P; Sinonquel, P; Abdelrahim, M; Ahmad, O; Berzin, T; Bhandari, P; Bretthauer, M; Coimbra, M; Dekker, E; Ebigbo, A; Eelbode, T; Frazzoni, L; Gross, SA; Ishihara, R; Kaminski, MF; Messmann, H; Mori, Y; Padoy, N; Parasa, S; Pilonis, ND; Renna, F; Repici, A; Simsek, C; Spadaccini, M; Bisschops, R; Bergman, JJGHM; Hassan, C; Ribeiro, MD;

Publication
GUT

Abstract
Artificial intelligence (AI) holds significant potential for enhancing quality of gastrointestinal (GI) endoscopy, but the adoption of AI in clinical practice is hampered by the lack of rigorous standardisation and development methodology ensuring generalisability. The aim of the Quality Assessment of pre-clinical AI studies in Diagnostic Endoscopy (QUAIDE) Explanation and Checklist was to develop recommendations for standardised design and reporting of preclinical AI studies in GI endoscopy. The recommendations were developed based on a formal consensus approach with an international multidisciplinary panel of 32 experts among endoscopists and computer scientists. The Delphi methodology was employed to achieve consensus on statements, with a predetermined threshold of 80% agreement. A maximum three rounds of voting were permitted. Consensus was reached on 18 key recommendations, covering 6 key domains: data acquisition and annotation (6 statements), outcome reporting (3 statements), experimental setup and algorithm architecture (4 statements) and result presentation and interpretation (5 statements). QUAIDE provides recommendations on how to properly design (1. Methods, statements 1-14), present results (2. Results, statements 15-16) and integrate and interpret the obtained results (3. Discussion, statements 17-18). The QUAIDE framework offers practical guidance for authors, readers, editors and reviewers involved in AI preclinical studies in GI endoscopy, aiming at improving design and reporting, thereby promoting research standardisation and accelerating the translation of AI innovations into clinical practice.

2025

Pulmonary Hypertension Detection from Heart Sound Analysis

Authors
Gaudio, A; Giordano, N; Elhilali, M; Schmidt, S; Renna, F;

Publication
IEEE Transactions on Biomedical Engineering

Abstract
The detection of Pulmonary Hypertension (PH) from the computer analysis of digitized heart sounds is a low-cost and non-invasive solution for early PH detection and screening. We present an extensive cross-domain evaluation methodology with varying animals (humans and porcine animals) and varying auscultation technologies (phonocardiography and seisomocardiography) evaluated across four methods. We introduce PH-ELM, a resource-efficient PH detection model based on the extreme learning machine that is smaller (300× fewer parameters), energy efficient (532× fewer watts of power), faster (36× faster to train, 44× faster at inference), and more accurate on out-of-distribution testing (improves median accuracy by 0.09 area under the ROC curve (auROC)) in comparison to a previously best performing deep network. We make four observations from our analysis: (a) digital auscultation is a promising technology for the detection of pulmonary hypertension; (b) seismocardiography (SCG) signals and phonocardiography (PCG) signals are interchangeable to train PH detectors; (c) porcine heart sounds in the training data can be used to evaluate PH from human heart sounds (the PH-ELM model preserves 88 to 95% of the best in-distribution baseline performance); (d) predictive performance of PH detection can be mostly preserved with as few as 10 heartbeats and capturing up to approximately 200 heartbeats per subject can improve performance. © 1964-2012 IEEE.

2024

Separation of the Aortic and Pulmonary Components of the Second Heart Sound via Alternating Optimization

Authors
Renna, F; Gaudio, A; Mattos, S; Plumbley, MD; Coimbra, MT;

Publication
IEEE ACCESS

Abstract
An algorithm for blind source separation (BSS) of the second heart sound (S2) into aortic and pulmonary components is proposed. It recovers aortic (A2) and pulmonary (P2) waveforms, as well as their relative delays, by solving an alternating optimization problem on the set of S2 sounds, without the use of auxiliary ECG or respiration phase measurement data. This unsupervised and data-driven approach assumes that the A2 and P2 components maintain the same waveform across heartbeats and that the relative delay between onset of the components varies according to respiration phase. The proposed approach is applied to synthetic heart sounds and to real-world heart sounds from 43 patients. It improves over two state-of-the-art BSS approaches by 10% normalized root mean-squared error in the reconstruction of aortic and pulmonary components using synthetic heart sounds, demonstrates robustness to noise, and recovery of splitting delays. The detection of pulmonary hypertension (PH) in a Brazilian population is demonstrated by training a classifier on three scalar features from the recovered A2 and P2 waveforms, and this yields an auROC of 0.76.

2024

Diffusion Model for Generating Synthetic Contrast Enhanced CT from Non-Enhanced Heart Axial CT Images

Authors
Ferreira V.R.S.; de Paiva A.C.; Silva A.C.; de Almeida J.D.S.; Junior G.B.; Renna F.;

Publication
International Conference on Enterprise Information Systems, ICEIS - Proceedings

Abstract
This work proposes the use of a deep learning-based adversarial diffusion model to address the translation of contrast-enhanced from non-contrast-enhanced computed tomography (CT) images of the heart. The study overcomes challenges in medical image translation by combining concepts from generative adversarial networks (GANs) and diffusion models. Results were evaluated using the Peak signal to noise ratio (PSNR) and structural index similarity (SSIM) to demonstrate the model's effectiveness in generating contrast images while preserving quality and visual similarity. Despite successes, Root Mean Square Error (RMSE) analysis indicates persistent challenges, highlighting the need for continuous improvements. The intersection of GANs and diffusion models promises future advancements, significantly contributing to clinical practice. The table compares CyTran, CycleGAN, and Pix2Pix networks with the proposed model, indicating directions for improvement.

2024

Lightweight 3D CNN for the Segmentation of Coronary Calcifications and Calcium Scoring

Authors
Santos, R; Baeza, R; Filipe, VM; Renna, F; Paredes, H; Pedrosa, J;

Publication
2024 IEEE 22ND MEDITERRANEAN ELECTROTECHNICAL CONFERENCE, MELECON 2024

Abstract
Coronary artery calcium is a good indicator of coronary artery disease and can be used for cardiovascular risk stratification. Over the years, different deep learning approaches have been proposed to automatically segment coronary calcifications in computed tomography scans and measure their extent through calcium scores. However, most methodologies have focused on using 2D architectures which neglect most of the information present in those scans. In this work, we use a 3D convolutional neural network capable of leveraging the 3D nature of computed tomography scans and including more context in the segmentation process. In addition, the selected network is lightweight, which means that we can have 3D convolutions while having low memory requirements. Our results show that the predictions of the model, trained on the COCA dataset, are close to the ground truth for the majority of the patients in the test set obtaining a Dice score of 0.90 +/- 0.16 and a Cohen's linearly weighted kappa of 0.88 in Agatston score risk categorization. In conclusion, our approach shows promise in the tasks of segmenting coronary artery calcifications and predicting calcium scores with the objectives of optimizing clinical workflow and performing cardiovascular risk stratification.

Supervised
thesis

2023

Deep Learning Algorithms for Anatomical Landmark Detection

Author
Miguel Lopes Martins

Institution
UP-FCUP

2023

Multimodal deep learning for heart sound and electrocardiogram classification

Author
Hélder Miguel Carvalho Vieira

Institution
UP-FCUP

2023

Novel deep learning methods for characterization of precancerous tissue in endoscopic narrow band images

Author
Maria Pedroso da Silva

Institution
UP-FCUP

2023

Listening for Wolf Conservation: Deep Learning for Automated Howl Recognition  and Classification

Author
Rafael de Faria Campos

Institution
UP-FCUP

2022

Automatic contrast generation from contrastless CTs

Author
Rúben André Dias Domingues

Institution
UP-FCUP