Evidence map›Paper›PMID 39001915›Full record

ArticleEuropean archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery2024

Artificial intelligence for automatic detection and segmentation of nasal polyposis: a pilot study.

Vittorio Rampinelli, Alberto Paderno, Carlo Conti, Gabriele Testa, Claudia Lodovica Modesti, Edoardo Agosti, Isabelle Dohin, Tommaso Saccardo, Alessandro Vinciguerra, Marco Ferrari and 5 more

Abstract read
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In one paragraph

Article in European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Artificial Intelligence in Rhinology: A State-of-the-Art Review of Clinical Readiness and Implementation Pathways.Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery · 2026
    Review
  3. Article
  4. Article
  5. Should computed tomography be used in the evaluation of biologic therapies for severe chronic rhinosinusitis with nasal polyps?Acta otorhinolaryngologica Italica : organo ufficiale della Societa italiana di otorinolaringologia e chirurgia cervico-facciale · 2025
    Article
  6. 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

15 authors.

Vittorio Rampinelli *Unit of Otorhinolaryngology - Head and Neck Surgery, Department of Surgical and Medical Specialties, Radiological Sciences, and Public Health, School of Medicine, ASST Spedali Civili, University of Brescia, Brescia, Italy. vittorio.rampinelli@gmail.com.ORCID http://orcid.org/0000-0002-0901-6266
Alberto Paderno *Otorhinolaryngology Unit, IRCCS Humanitas Research Hospital, Milano, Italy.
Carlo ContiUnit of Otorhinolaryngology - Head and Neck Surgery, Department of Surgical and Medical Specialties, Radiological Sciences, and Public Health, School of Medicine, ASST Spedali Civili, University of Brescia, Brescia, Italy.
Gabriele TestaUnit of Otorhinolaryngology - Head and Neck Surgery, Department of Surgical and Medical Specialties, Radiological Sciences, and Public Health, School of Medicine, ASST Spedali Civili, University of Brescia, Brescia, Italy.
Claudia Lodovica ModestiUnit of Otorhinolaryngology - Head and Neck Surgery, Department of Surgical and Medical Specialties, Radiological Sciences, and Public Health, School of Medicine, ASST Spedali Civili, University of Brescia, Brescia, Italy.
Edoardo AgostiDivision of Neurosurgery, Department of Surgical and Medical Specialties, Radiological Sciences, and Public Health, School of Medicine, ASST Spedali Civili, University of Brescia, Brescia, Italy.
Isabelle DohinUnit of Otorhinolaryngology - Head and Neck Surgery, Department of Surgical and Medical Specialties, Radiological Sciences, and Public Health, School of Medicine, ASST Spedali Civili, University of Brescia, Brescia, Italy.
Tommaso SaccardoSection of Otorhinolaryngology - Head and Neck Surgery, Department of Neurosciences, University of Padova, Padova, PD, Italy.
Alessandro VinciguerraOtorhinolaryngology and Skull Base Center, AP-HP, Hospital Lariboisière, Paris, France.
Marco FerrariSection of Otorhinolaryngology - Head and Neck Surgery, Department of Neurosciences, University of Padova, Padova, PD, Italy.
Alberto SchreiberUnit of Otorhinolaryngology - Head and Neck Surgery, Department of Surgical and Medical Specialties, Radiological Sciences, and Public Health, School of Medicine, ASST Spedali Civili, University of Brescia, Brescia, Italy.
Davide MattavelliUnit of Otorhinolaryngology - Head and Neck Surgery, Department of Surgical and Medical Specialties, Radiological Sciences, and Public Health, School of Medicine, ASST Spedali Civili, University of Brescia, Brescia, Italy.
Piero NicolaiSection of Otorhinolaryngology - Head and Neck Surgery, Department of Neurosciences, University of Padova, Padova, PD, Italy.
Chris HolsingerDivision of Head and Neck Surgery, Department of Otolaryngology, Stanford University, Palo Alto, CA, USA.
Cesare PiazzaUnit of Otorhinolaryngology - Head and Neck Surgery, Department of Surgical and Medical Specialties, Radiological Sciences, and Public Health, School of Medicine, ASST Spedali Civili, University of Brescia, Brescia, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeAccurate diagnosis and quantification of polyps and symptoms are pivotal for planning the therapeutic strategy of Chronic rhinosinusitis with nasal polyposis (CRSwNP). This pilot study aimed to develop an artificial intelligence (AI)-based image analysis system capable of segmenting nasal polyps from nasal endoscopy videos.

methodsRecorded nasal videoendoscopies from 52 patients diagnosed with CRSwNP between 2019 and 2022 were retrospectively analyzed. Images extracted were manually segmented on the web application Roboflow. A dataset of 342 images was generated and divided into training (80%), validation (10%), and testing (10%) sets. The Ultralytics YOLOv8.0.28 model was employed for automated segmentation.

resultsThe YOLOv8s-seg model consisted of 195 layers and required 42.4 GFLOPs for operation. When tested against the validation set, the algorithm achieved a precision of 0.91, recall of 0.839, and mean average precision at 50% IoU (mAP50) of 0.949. For the segmentation task, similar metrics were observed, including a mAP ranging from 0.675 to 0.679 for IoUs between 50% and 95%.

conclusionsThe study shows that a carefully trained AI algorithm can effectively identify and delineate nasal polyps in patients with CRSwNP. Despite certain limitations like the focus on CRSwNP-specific samples, the algorithm presents a promising complementary tool to existing diagnostic methods.

Indexed as

Artificial IntelligenceEndoscopyNasal PolypsAlgorithmsChronic DiseaseFemaleHumansImage Processing, Computer-AssistedMalePilot ProjectsRetrospective StudiesRhinitisSinusitisVideo RecordingArtificial intelligenceData analysisDiagnosisNasal polypsSinusitis

Identifiers

PMID39001915

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Registered trials

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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.