Your browser doesn't support javascript.
loading
Study protocol for a two-site clinical trial to validate a smartphone-based artificial intelligence classifier identifying cervical precancer and cancer in HPV-positive women in Cameroon.
Baleydier, Inès; Vassilakos, Pierre; Viñals, Roser; Wisniak, Ania; Kenfack, Bruno; Tsuala Fouogue, Jovanny; Enownchong Enow Orock, George; Lemoupa Makajio, Sophie; Foguem Tincho, Evelyn; Undurraga, Manuela; Cattin, Magali; Makohliso, Solomzi; Schönenberger, Klaus; Gervaix, Alain; Thiran, Jean-Philippe; Petignat, Patrick.
Afiliação
  • Baleydier I; University of Geneva, Faculty of Medicine, Geneva, Switzerland.
  • Vassilakos P; Department of Pediatrics, Gynecology and Obstetrics, University Hospitals of Geneva, Geneva, Switzerland.
  • Viñals R; Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
  • Wisniak A; Department of Pediatrics, Gynecology and Obstetrics, University Hospitals of Geneva, Geneva, Switzerland.
  • Kenfack B; Department of Obstetrics Gynecology and Maternal Health, Dschang District Hospital, University of Dschang, Dschang, Cameroon.
  • Tsuala Fouogue J; Department of Obstetrics Gynecology and Maternal Health, Dschang District Hospital, University of Dschang, Dschang, Cameroon.
  • Enownchong Enow Orock G; Regional Hospital of Bafoussam, Bafoussam, Cameroon.
  • Lemoupa Makajio S; Regional Hospital of Bafoussam, Bafoussam, Cameroon.
  • Foguem Tincho E; Department of Pediatrics, Gynecology and Obstetrics, University Hospitals of Geneva, Geneva, Switzerland.
  • Undurraga M; Department of Obstetrics Gynecology and Maternal Health, Dschang District Hospital, University of Dschang, Dschang, Cameroon.
  • Cattin M; Department of Obstetrics Gynecology and Maternal Health, Dschang District Hospital, University of Dschang, Dschang, Cameroon.
  • Makohliso S; Department of Pediatrics, Gynecology and Obstetrics, University Hospitals of Geneva, Geneva, Switzerland.
  • Schönenberger K; Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
  • Gervaix A; EssentialTech Centre, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
  • Thiran JP; Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
  • Petignat P; Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
PLoS One ; 16(12): e0260776, 2021.
Article em En | MEDLINE | ID: mdl-34914727
ABSTRACT

INTRODUCTION:

Cervical cancer remains a major public health challenge in low- and middle-income countries (LMICs) due to financial and logistical issues. WHO recommendation for cervical cancer screening in LMICs includes HPV testing as primary screening followed by visual inspection with acetic acid (VIA) and treatment. However, VIA is a subjective procedure dependent on the healthcare provider's experience. Its accuracy can be improved by computer-aided detection techniques. Our aim is to assess the performance of a smartphone-based Automated VIA Classifier (AVC) relying on Artificial Intelligence to discriminate precancerous and cancerous lesions from normal cervical tissue.

METHODS:

The AVC study will be nested in an ongoing cervical cancer screening program called "3T-study" (for Test, Triage and Treat), including HPV self-sampling followed by VIA triage and treatment if needed. After application of acetic acid on the cervix, precancerous and cancerous cells whiten more rapidly than non-cancerous ones and their whiteness persists stronger overtime. The AVC relies on this key feature to determine whether the cervix is suspect for precancer or cancer. In order to train and validate the AVC, 6000 women aged 30 to 49 years meeting the inclusion criteria will be recruited on a voluntary basis, with an estimated 100 CIN2+, calculated using a confidence level of 95% and an estimated sensitivity of 90% +/-7% precision on either side. Diagnostic test performance of AVC test and two current standard tests (VIA and cytology) used routinely for triage will be evaluated and compared. Histopathological examination will serve as reference standard. Participants' and providers' acceptability of the technology will also be assessed. The study protocol was registered under ClinicalTrials.gov (number NCT04859530). EXPECTED

RESULTS:

The study will determine whether AVC test can be an effective method for cervical cancer screening in LMICs.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Papillomaviridae / Inteligência Artificial / Displasia do Colo do Útero / Neoplasias do Colo do Útero / Infecções por Papillomavirus / Detecção Precoce de Câncer / Smartphone Tipo de estudo: Clinical_trials / Diagnostic_studies / Guideline / Observational_studies / Prognostic_studies / Risk_factors_studies / Screening_studies Limite: Adult / Female / Humans / Middle aged País/Região como assunto: Africa Idioma: En Revista: PLoS One Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Papillomaviridae / Inteligência Artificial / Displasia do Colo do Útero / Neoplasias do Colo do Útero / Infecções por Papillomavirus / Detecção Precoce de Câncer / Smartphone Tipo de estudo: Clinical_trials / Diagnostic_studies / Guideline / Observational_studies / Prognostic_studies / Risk_factors_studies / Screening_studies Limite: Adult / Female / Humans / Middle aged País/Região como assunto: Africa Idioma: En Revista: PLoS One Ano de publicação: 2021 Tipo de documento: Article