Application of Machine Learning to Ultrasonography in Identifying Anatomical Landmarks for Cricothyroidotomy Among Female Adults: A Multi-center Prospective Observational Study.
We aimed to develop machine learning (ML)-based algorithms to assist physicians in ultrasound-guided localization of cricoid cartilage (CC) and thyroid cartilage (TC) in cricothyroidotomy. Adult female volunteers were prospectively recruited from two hospitals between September and December, 2020. U...
| Published in: | Journal of Digital Imaging Vol. 37; no. 1; pp. 363 - 374 |
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| Main Authors: | , , , , , , , , , |
| Format: | diagnostic images research tables/charts Journal Article |
| Published: |
Springer Nature
Feb2024
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=175966520&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 175966520 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Feb2024 vid: 37 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 175966520 175966520 175966520 10.1007/s10278-023-00929-3 175966520 ppf: 363 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Application of Machine Learning to Ultrasonography in Identifying Anatomical Landmarks for Cricothyroidotomy Among Female Adults: A Multi-center Prospective Observational Study. aug: au: Wang, Chih-Hung Li, Jia-Da Wu, Cheng-Yi Wu, Yu-Chen Tay, Joyce Wu, Meng-Che Hsu, Ching-Hang Liu, Yi-Kuan Chen, Chu-Song Huang, Chien-Hua affil: https://ror.org/03nteze27 Department of Emergency Medicine, National Taiwan University Hospital, Taipei, Taiwan sug: subj: Machine Learning Methods Ultrasonography Methods Cricothyrotomy In Adulthood Women's Health Human Female Adult Multicenter Studies Prospective Studies Nonexperimental Studies Cricoid Cartilage Ultrasonography Thyroid Cartilage Ultrasonography Neural Networks (Computer) Volunteer Workers Algorithms Deep Learning Methods Hospitals Health Personnel Psychosocial Factors Descriptive Statistics ROC Curve Confidence Intervals Adult: 19-44 years Female ab: We aimed to develop machine learning (ML)-based algorithms to assist physicians in ultrasound-guided localization of cricoid cartilage (CC) and thyroid cartilage (TC) in cricothyroidotomy. Adult female volunteers were prospectively recruited from two hospitals between September and December, 2020. Ultrasonographic images were collected via a modified longitudinal technique. You Only Look Once (YOLOv5s), Faster Regions with Convolutional Neural Network features (Faster R-CNN), and Single Shot Detector (SSD) were selected as the model architectures. A total of 488 women (mean age: 36.0 years) participated in the study, contributing to a total of 292,053 frames of ultrasonographic images. The derived ML-based algorithms demonstrated excellent discriminative performance for the presence of CC (area under the receiver operating characteristic curve [AUC]: YOLOv5s, 0.989, 95% confidence interval [CI]: 0.982–0.994; Faster R-CNN, 0.986, 95% CI: 0.980–0.991; SSD, 0.968, 95% CI: 0.956–0.977) and TC (AUC: YOLOv5s, 0.989, 95% CI: 0.977–0.997; Faster R-CNN, 0.981, 95% CI: 0.965–0.991; SSD, 0.982, 95% CI: 0.973–0.990). Furthermore, in the frames where the model could correctly indicate the presence of CC or TC, it also accurately localized CC (intersection-over-union: YOLOv5s, 0.753, 95% CI: 0.739–0.765; Faster R-CNN, 0.720, 95% CI: 0.709–0.732; SSD, 0.739, 95% CI: 0.726–0.751) or TC (intersection-over-union: YOLOv5s, 0.739, 95% CI: 0.722–0.755; Faster R-CNN, 0.709, 95% CI: 0.687–0.730; SSD, 0.713, 95% CI: 0.695–0.730). The ML-based algorithms could identify anatomical landmarks for cricothyroidotomy in adult females with favorable discriminative and localization performance. Further studies are warranted to transfer this algorithm to hand-held portable ultrasound devices for clinical use. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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