{
  "abstract": "Objective Capsule endoscopy (CE) provides a minimally invasive exam modality for panendoscopic evaluation of the entire gastrointestinal (GI) tract. However, conventional reading methods can be time-consuming and error-prone. Protruding lesions are a relatively common entity that can be found with a variable incidence and different pathological significance throughout the GI tract. The aim of this study was to develop and test a convolutional neural network (CNN)-based algorithm for panendoscopic automatic detection of protruding lesions on CE exams.Methods A multicentric retrospective study was conducted, based on 1245 CE exams. We used a total of 191 455 frames, from six types of CE devices, of which 52 717 had protruding lesions (polyps, epithelial tumours or subepithelial lesions) after triple validation. Data were divided into a training and test set (90% vs 10%), in an exam-split design. During the training stage, we performed a fivefold cross-validation. Our outcome measures were sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and areas under the conventional receiver operating characteristic curve (AUC-ROC) and the precision-recall curve (AUC-PR).Results In the test set, the sensitivity was 79.7% and the specificity was 96.5%. The PPV and NPV were 81.5% and 96.0%, respectively. The global accuracy was 93.7%.Conclusion This study aims to address a gap in artificial intelligence (AI)-enhanced capsule panendoscopy by reporting the development of the first CNN for the detection of protruding lesions across the GI tract. AI’s improvement of CE’s diagnostic accuracy, along with the growing interest in minimally invasive procedures, may contribute to increasing access to this diagnostic tool. Further multicentric and prospective studies are needed to validate our preliminary results to ultimately introduce deep learning models into clinical practice.",
  "authors": [
    {
      "affiliations": [
        "Department of Gastroenterology, University Hospital Centre of Sao Joao, Porto, Portugal",
        "Universidade do Porto Faculdade de Medicina, Porto, Portugal"
      ],
      "name": "Miguel José Mascarenhas Saraiva"
    },
    {
      "affiliations": [
        "Department of Gastroenterology, University Hospital Centre of Sao Joao, Porto, Portugal"
      ],
      "name": "Maria João Almeida"
    },
    {
      "affiliations": [
        "Universidade do Porto Faculdade de Medicina, Porto, Portugal",
        "Department of Gastroenterology, São João University Hospital, Porto, Portugal"
      ],
      "name": "Miguel Martins"
    },
    {
      "affiliations": [
        "Department of Gastroenterology, São João University Hospital, Porto, Portugal"
      ],
      "name": "João Afonso"
    },
    {
      "affiliations": [
        "Department of Gastroenterology, São João University Hospital, Porto, Portugal",
        "WGO Gastroenterology and Hepatology Training Centre, Porto, Portugal"
      ],
      "name": "Tiago Ribeiro"
    },
    {
      "affiliations": [
        "Department of Gastroenterology, São João University Hospital, Porto, Portugal",
        "WGO Gastroenterology and Hepatology Training Centre, Porto, Portugal"
      ],
      "name": "Pedro Marílio Moreira Sá Cardoso"
    },
    {
      "affiliations": [
        "Department of Gastroenterology, São João University Hospital, Porto, Portugal",
        "WGO Gastroenterology and Hepatology Training Centre, Porto, Portugal"
      ],
      "name": "Francisco Miguel Costa Silva Mendes"
    },
    {
      "affiliations": [
        "Department of Gastroenterology, São João University Hospital, Porto, Portugal",
        "WGO Gastroenterology and Hepatology Training Centre, Porto, Portugal"
      ],
      "name": "Joana Mota"
    },
    {
      "affiliations": [
        "Gastroenterology Department, Centro Hospitalar São João, Porto, Portugal",
        "Centro Hospitalar São João, Porto, Portugal"
      ],
      "name": "Ana Patricia Andrade"
    },
    {
      "affiliations": [
        "Gastroenterology Department, Centro Hospitalar São João, Porto, Portugal"
      ],
      "name": "Helder Cardoso"
    },
    {
      "affiliations": [
        "Department of Mechanical Engineering, Faculty of Engineering of the University of Porto, Porto, Portugal",
        "INEGI - Institute of Science and Innovation in Mechanical and Industrial Engineering, Porto, Portugal"
      ],
      "name": "João Ferreira"
    },
    {
      "affiliations": [
        "Gastroenterology Department, Centro Hospitalar São João, Porto, Portugal"
      ],
      "name": "Guilherme Macedo"
    }
  ],
  "title": "Deep learning and capsule endoscopy: automatic panendoscopic detection of protruding lesions",
  "uid": "3f5c0586-3457-54a6-a3d6-a96a3ac11888"
}