Evidence map›Paper›PMID 34914727›Full record

ArticlePloS one2021

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.

Inès Baleydier, Pierre Vassilakos, Roser Viñals, Ania Wisniak, Bruno Kenfack, Jovanny Tsuala Fouogue, George Enownchong Enow Orock, Sophie Lemoupa Makajio, Evelyn Foguem Tincho, Manuela Undurraga and 6 more

Registry-linked trialOpen access · goldAbstract readClinical Trial Protocol
PubMed Full text at PMC Publisher
In one paragraph

Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04859530 (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), which is not on this map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.6field-weighted citation impact, top 30% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT04859530 narecruitingnot on this map

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

TypeinterventionalSponsorProf. Patrick PetignatRan2018 to 2025Enrolled5,886ConditionsCervical Cancer, HPVArmsAVC test
3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors at 4 institutions in 2 countries.

Inès BaleydierUniversity of Geneva, Faculty of Medicine, Geneva, Switzerland.ORCID 0000-0001-6340-6815
Pierre VassilakosDepartment of Pediatrics, Gynecology and Obstetrics, University Hospitals of Geneva, Geneva, Switzerland.
Roser ViñalsSignal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
Ania WisniakDepartment of Pediatrics, Gynecology and Obstetrics, University Hospitals of Geneva, Geneva, Switzerland.
Bruno KenfackDepartment of Obstetrics Gynecology and Maternal Health, Dschang District Hospital, University of Dschang, Dschang, Cameroon.
Jovanny Tsuala FouogueDepartment of Obstetrics Gynecology and Maternal Health, Dschang District Hospital, University of Dschang, Dschang, Cameroon.ORCID 0000-0003-0055-0902
George Enownchong Enow OrockRegional Hospital of Bafoussam, Bafoussam, Cameroon.
Sophie Lemoupa MakajioDepartment of Pediatrics, Gynecology and Obstetrics, University Hospitals of Geneva, Geneva, Switzerland.
Evelyn Foguem TinchoDepartment of Obstetrics Gynecology and Maternal Health, Dschang District Hospital, University of Dschang, Dschang, Cameroon.
Manuela UndurragaDepartment of Pediatrics, Gynecology and Obstetrics, University Hospitals of Geneva, Geneva, Switzerland.
Magali CattinSignal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
Solomzi MakohlisoSignal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
Klaus SchönenbergerSignal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
Alain GervaixDepartment of Pediatrics, Gynecology and Obstetrics, University Hospitals of Geneva, Geneva, Switzerland.
Jean-Philippe ThiranSignal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
Patrick PetignatDepartment of Pediatrics, Gynecology and Obstetrics, University Hospitals of Geneva, Geneva, Switzerland.
École Polytechnique Fédérale de Lausanne · CHUniversity Hospital of Geneva · CHUniversité de Dschang · CMUniversity of Geneva · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionCervical 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.

methodsThe 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

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

Indexed as

Artificial IntelligenceEarly Detection of CancerPapillomaviridaePapillomavirus InfectionsSmartphoneUterine Cervical DysplasiaUterine Cervical NeoplasmsAcetic AcidAdultCameroonClinical Trials as TopicFemaleHumansMiddle AgedMulticenter Studies as TopicPrognosisAcetic Acid

Identifiers

PMID34914727
PMCPMC8675688
OpenAlexW4200581473

What OpenQuestion holds

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LicenceCC BY
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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.