Observational studyJournal of medical systems2025
Real-Time Identification of Cricothyrotomy Landmarks in Emergency Care and Obstetric Patients Using Wireless Handheld Ultrasound and Edge-Computing Artificial Intelligence: A Prospective Observational Study.
Observational study in Journal of medical systems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- AI-augmented cardiopulmonary point-of-care ultrasound: a narrative review for pulmonary and critical care clinicians.Breathe (Sheffield, England) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
This study aimed to develop machine learning-based algorithms to assist physicians in ultrasound-guided localization of the cricoid cartilage (CC), thyroid cartilage (TC), and cricothyroid membrane (CTM) for cricothyroidotomy. Adult female participants presenting to the emergency department with dyspnea or to the obstetrics and gynecology department for a scheduled cesarean section between August 2022 and July 2024 were prospectively recruited. Ultrasonographic images were collected using a wireless handheld ultrasound device connected to an edge computing tablet. Three You Only Look Once (YOLO) model variants-v5n6, v8n, and v10n-were selected for development and evaluation. A total of 608 participants (median age: 58.0 years, interquartile range [IQR]: 40.0-73.0; median body mass index: 23.2 kg/m², IQR: 20.2-26.5) contributed 117,094 ultrasonographic frames. All three YOLO-based models demonstrated high accuracy in detecting CC, TC, and CTM, with area under the receiver operating characteristic curve values exceeding 0.88. In correctly identified frames, the models effectively localized CC (IOU values: YOLOv5n6, 0.713 [95% confidence interval (CI): 0.698-0.726]; YOLOv8n, 0.718 [95% CI: 0.702-0.733]; YOLOv10n, 0.718 [95% CI: 0.701-0.734]; p value: 0.03) and TC (YOLOv5n6, 0.700 [95% CI: 0.683-0.717]; YOLOv8n, 0.706 [95% CI: 0.687-0.725]; YOLOv10n, 0.703 [95% CI: 0.783-0.721] ; p value: 0.037), though localization accuracy was lower for CTM (YOLOv5n6, 0.364 [95% CI: 0.333-0.394]; YOLOv8n, 0.363 [95% CI: 0.331-0.394]; YOLOv10n, 0.354 [95% CI: 0.325-0.381] ; p value: 0.053). The mean frames per second for YOLOv5n6, YOLOv8n, and YOLOv10n were 3.67, 13.83, and 14.13, respectively, when deployed on the handheld ultrasound platform. YOLO-based models demonstrated high accuracy in detecting and localizing CC, TC, and CTM. YOLOv8n and YOLOv10n achieved clinically acceptable real-time imaging performance when deployed on a wireless handheld ultrasound device with an edge computing tablet. Further studies are needed to assess whether this favorable performance translates into actual clinical benefits.
Indexed as
Identifiers
41068530What OpenQuestion holds
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.