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Details

  • Name

    Francesco Renna
  • Role

    External Research Collaborator
  • Since

    01st June 2020
  • Nationality

    Itália
  • Contacts

    +351222094000
    francesco.renna@inesctec.pt
001
Publications

2021

Standalone performance of artificial intelligence for upper GI neoplasia: a meta-analysis

Authors
Arribas, J; Antonelli, G; Frazzoni, L; Fuccio, L; Ebigbo, A; van der Sommen, F; Ghatwary, N; Palm, C; Coimbra, M; Renna, F; Bergman, JJGHM; Sharma, P; Messmann, H; Hassan, C; Dinis Ribeiro, MJ;

Publication
GUT

Abstract
Objective Artificial intelligence (AI) may reduce underdiagnosed or overlooked upper GI (UGI) neoplastic and preneoplastic conditions, due to subtle appearance and low disease prevalence. Only disease-specific AI performances have been reported, generating uncertainty on its clinical value. Design We searched PubMed, Embase and Scopus until July 2020, for studies on the diagnostic performance of AI in detection and characterisation of UGI lesions. Primary outcomes were pooled diagnostic accuracy, sensitivity and specificity of AI. Secondary outcomes were pooled positive (PPV) and negative (NPV) predictive values. We calculated pooled proportion rates (%), designed summary receiving operating characteristic curves with respective area under the curves (AUCs) and performed metaregression and sensitivity analysis. Results Overall, 19 studies on detection of oesophageal squamous cell neoplasia (ESCN) or Barrett's esophagus-related neoplasia (BERN) or gastric adenocarcinoma (GCA) were included with 218, 445, 453 patients and 7976, 2340, 13 562 images, respectively. AI-sensitivity/specificity/PPV/NPV/positive likelihood ratio/negative likelihood ratio for UGI neoplasia detection were 90% (CI 85% to 94%)/89% (CI 85% to 92%)/87% (CI 83% to 91%)/91% (CI 87% to 94%)/8.2 (CI 5.7 to 11.7)/0.111 (CI 0.071 to 0.175), respectively, with an overall AUC of 0.95 (CI 0.93 to 0.97). No difference in AI performance across ESCN, BERN and GCA was found, AUC being 0.94 (CI 0.52 to 0.99), 0.96 (CI 0.95 to 0.98), 0.93 (CI 0.83 to 0.99), respectively. Overall, study quality was low, with high risk of selection bias. No significant publication bias was found. Conclusion We found a high overall AI accuracy for the diagnosis of any neoplastic lesion of the UGI tract that was independent of the underlying condition. This may be expected to substantially reduce the miss rate of precancerous lesions and early cancer when implemented in clinical practice.

2021

Joint Training of Hidden Markov Model and Neural Network for Heart Sound Segmentation

Authors
Renna, F; Martins, ML; Coimbra, MT;

Publication
Computing in Cardiology, CinC 2021, Brno, Czech Republic, September 13-15, 2021

Abstract

2021

Source Separation of the Second Heart Sound via Alternating Optimization

Authors
Renna, F; Plumbley, MD; Coimbra, MT;

Publication
Computing in Cardiology, CinC 2021, Brno, Czech Republic, September 13-15, 2021

Abstract

2021

Do we really need a segmentation step in heart sound classification algorithms?

Authors
Oliveira, J; Nogueira, D; Renna, F; Ferreira, C; Jorge, AM; Coimbra, M;

Publication
2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC)

Abstract

2021

The CirCor DigiScope Dataset: From Murmur Detection to Murmur Classification

Authors
Oliveira J.H.; Renna F.; Costa P.; Nogueira D.; Oliveira C.; Ferreira C.; Jorge A.; Mattos S.; Hatem T.; Tavares T.; Elola A.; Rad A.; Sameni R.; Clifford G.D.; Coimbra M.T.;

Publication
IEEE Journal of Biomedical and Health Informatics

Abstract