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Details

  • Name

    Miguel Coimbra
  • Role

    TEC4 Coordinator
  • Since

    15th September 1998
  • Nationality

    Portugal
  • Contacts

    +351222094106
    miguel.coimbra@inesctec.pt
003
Publications

2022

The CirCor DigiScope Dataset: From Murmur Detection to Murmur Classification

Authors
Oliveira, J; Renna, F; Costa, PD; Nogueira, M; Oliveira, C; Ferreira, C; Jorge, A; Mattos, S; Hatem, T; Tavares, T; Elola, A; Rad, AB; Sameni, R; Clifford, GD; Coimbra, MT;

Publication
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS

Abstract

2022

Artificial Intelligence for Upper Gastrointestinal Endoscopy: A Roadmap from Technology Development to Clinical Practice

Authors
Renna, F; Martins, M; Neto, A; Cunha, A; Libanio, D; Dinis-Ribeiro, M; Coimbra, M;

Publication
DIAGNOSTICS

Abstract
Stomach cancer is the third deadliest type of cancer in the world (0.86 million deaths in 2017). In 2035, a 20% increase will be observed both in incidence and mortality due to demographic effects if no interventions are foreseen. Upper GI endoscopy (UGIE) plays a paramount role in early diagnosis and, therefore, improved survival rates. On the other hand, human and technical factors can contribute to misdiagnosis while performing UGIE. In this scenario, artificial intelligence (AI) has recently shown its potential in compensating for the pitfalls of UGIE, by leveraging deep learning architectures able to efficiently recognize endoscopic patterns from UGIE video data. This work presents a review of the current state-of-the-art algorithms in the application of AI to gastroscopy. It focuses specifically on the threefold tasks of assuring exam completeness (i.e., detecting the presence of blind spots) and assisting in the detection and characterization of clinical findings, both gastric precancerous conditions and neoplastic lesion changes. Early and promising results have already been obtained using well-known deep learning architectures for computer vision, but many algorithmic challenges remain in achieving the vision of AI-assisted UGIE. Future challenges in the roadmap for the effective integration of AI tools within the UGIE clinical practice are discussed, namely the adoption of more robust deep learning architectures and methods able to embed domain knowledge into image/video classifiers as well as the availability of large, annotated datasets.

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, M;

Publication
2021 COMPUTING IN CARDIOLOGY (CINC)

Abstract

2021

Source Separation of the Second Heart Sound via Alternating Optimization

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

Publication
2021 COMPUTING IN CARDIOLOGY (CINC)

Abstract

Supervised
thesis

2020

Diagnosis of Rheumatic Heart Diseases based in Phonocardiograms and Echocardiograms

Author
Diogo Marcelo Esterlita Nogueira

Institution
UP-FCUP

2020

Criação de algoritmos de Deep Learning para identificação de tecidos nas histeroscopias

Author
Ana Sofia Ferreira Martins

Institution
UP-FCUP

2020

Changing Perspectives: Interlead Conversion in Electrocardiographic Signals

Author
Carolina Martins Barbosa Rodrigues Afonso

Institution
UP-FCUP

2019

Utilização de algoritmos de visão computacional para diagnóstico de doenças da vinha

Author
Simon Afonso

Institution
UP-FCUP

2019

Crackle and wheeze detection in lung sound signals using convolutional neural networks

Author
Pedro Sousa Faustino

Institution
UP-FCUP