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Detalhes

Detalhes

  • Nome

    Miguel Coimbra
  • Cargo

    Coordenador de TEC4
  • Desde

    15 setembro 1998
  • Nacionalidade

    Portugal
  • Contactos

    +351222094106
    miguel.coimbra@inesctec.pt
003
Publicações

2021

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

Autores
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;

Publicação
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

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

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

Abstract

2021

Source Separation of the Second Heart Sound via Alternating Optimization

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

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

Abstract

2021

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

Autores
Faustino, P; Oliveira, J; Coimbra, M;

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

Abstract

2021

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

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

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

Abstract

Teses
supervisionadas

2020

Diagnosis of Rheumatic Heart Diseases based in Phonocardiograms and Echocardiograms

Autor
Diogo Marcelo Esterlita Nogueira

Instituição
UP-FCUP

2020

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

Autor
Ana Sofia Ferreira Martins

Instituição
UP-FCUP

2020

Changing Perspectives: Interlead Conversion in Electrocardiographic Signals

Autor
Carolina Martins Barbosa Rodrigues Afonso

Instituição
UP-FCUP

2019

Deep Learning techniques in Object Recognition

Autor
Nuno Miguel Santos Marques

Instituição
UP-FCUP

2019

Real-time analysis of vital sign signals for online health monitoring in Unsupervised environments

Autor
Can Ye

Instituição
UP-FCUP