Evidence map›Paper›PMID 41608015›Full record

ArticleJournal of medical signals and sensors2026

Isfahan Artificial Intelligence Event 2024, Challenge I: Respiratory Depression Detection.

Azra Rasouli Kenari, Neda Esmaeili, Mahnoosh Tajmirriahi, Mehdi Khashei, Morteza Ebrahimpour, Pariya Alinezhad, Ehsan Sheikhi, Ali Loghmani, Mohammad Reza Torabi, Mehdi Abruee and 6 more

Abstract read
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In one paragraph

Article in Journal of medical signals and sensors, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Azra Rasouli KenariMedical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
Neda EsmaeiliDivision of Sleep and Circadian Disorders, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.
Mahnoosh TajmirriahiMedical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
Mehdi KhasheiDepartment of Industrial and Systems Engineering, Isfahan University of Technologies, Isfahan, Iran.
Morteza EbrahimpourDepartment of Industrial and Systems Engineering, Isfahan University of Technologies, Isfahan, Iran.
Pariya AlinezhadDepartment of Industrial and Systems Engineering, Isfahan University of Technologies, Isfahan, Iran.
Ehsan SheikhiDepartment of Industrial and Systems Engineering, Isfahan University of Technologies, Isfahan, Iran.
Ali LoghmaniDepartment of Mechanical Engineering, Isfahan University of Technology, Isfahan, Iran.
Mohammad Reza TorabiDepartment of Mechanical Engineering, Isfahan University of Technology, Isfahan, Iran.
Mehdi AbrueeDepartment of Mechanical Engineering, Isfahan University of Technology, Isfahan, Iran.
Mohamad KianiDepartment of Computer Engineering, Isfahan University, Isfahan, Iran.
Farzad NekoueiDepartment of Computer Engineering, Isfahan University, Isfahan, Iran.
Mohamad Yasin FakharDepartment of Computer Engineering, Isfahan University, Isfahan, Iran.
Mahmoud SaghaeiAnesthesiology and Critical Care Research Center, Isfahan University of Medical Sciences, Isfahan, Iran.
Mohammad Hassan MoradiDepartment of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran.
Hossein RabbaniMedical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The use of sedative drugs during various medical procedures is on the rise, necessitating close monitoring of respiratory function throughout the administration process. Continuous auscultation of tracheal sounds is an effective method for monitoring respiratory status. However, it requires constant attention from the operator, which may not always be feasible. Methods: This concept led to the development of a tracheal sound dataset featuring recordings from 16 patients who underwent cataract surgery at Alzahra Hospital, a university hospital in Isfahan, Iran. To ensure accuracy, the dataset was carefully examined with the assistance of an anesthesiology team, providing precise ground truth annotations for respiratory depression (RD) intervals at a resolution of one second. The Isfahan National Elite Foundation hosted the Isfahan artificial intelligence (AI) 2024 events to advance AI-based detection technologies and offered financial support for five challenges, including the competition for detecting RD from tracheal sounds. Twelve teams from various provinces across Iran participated, utilizing a shared dataset for their evaluations. Results: The teams that achieved the first through third places were Houshmandsazan, Houshava, and Hoopad, with F1-Scores of 65.18%, 50.44%, and 21.73%, respectively. All participating teams utilized deep learning techniques to detect RD intervals, achieving notable performance, yet opportunities for further improvement remain. Conclusion: This paper summarizes the performance of these teams, detailing the metrics used to assess their results and the methodologies employed by the top three competitors.

Indexed as

Apneaartificial intelligencedeep learningIsfahan artificial intelligence event 2024respiratory depressiontracheal sound

Identifiers

PMID41608015
PMCPMC12844833

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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.