Patient's airway monitoring during cardiopulmonary resuscitation using deep networks (2024)

Marhamati, M., Dorry, B., Imannezhad, S., Hussain, M. A., Neshat, A. A., Kalmishi, A. (2024). Patient's airway monitoring during cardiopulmonary resuscitation using deep networks. Medical Engineering and Physics, 129, Artikkeli 104179. https://doi.org/10.1016/j.medengphy.2024.104179

Marhamati, Mahmoud ; Dorry, Behnam ; Imannezhad, Shima et al. / Patient's airway monitoring during cardiopulmonary resuscitation using deep networks. Julkaisussa: Medical Engineering and Physics. 2024 ; Vuosikerta 129.

@article{a9e87891ca32422897ca0a42008e7c32,

title = "Patient's airway monitoring during cardiopulmonary resuscitation using deep networks",

abstract = "Cardiopulmonary resuscitation (CPR) is a crucial life-saving technique commonly administered to individuals experiencing cardiac arrest. Among the important aspects of CPR is ensuring the correct airway position of the patient, which is typically monitored by human tutors or supervisors. This study aims to utilize deep transfer learning for the detection of the patient's correct and incorrect airway position during cardiopulmonary resuscitation. To address the challenge of identifying the airway position, we curated a dataset consisting of 198 recorded video sequences, each lasting 6–8 s, showcasing both correct and incorrect airway positions during mouth-to-mouth breathing and breathing with an Ambu Bag. We employed six cutting-edge deep networks, namely DarkNet19, EfficientNetB0, GoogleNet, MobileNet-v2, ResNet50, and NasnetMobile. These networks were initially pre-trained on computer vision data and subsequently fine-tuned using the CPR dataset. The validation of the fine-tuned networks in detecting the patient's correct airway position during mouth-to-mouth breathing achieved impressive results, with the best sensitivity (98.8 %), specificity (100 %), and F-measure (97.2 %). Similarly, the detection of the patient's correct airway position during breathing with an Ambu Bag exhibited excellent performance, with the best sensitivity (100 %), specificity (99.8 %), and F-measure (99.7 %).",

keywords = "Artificial intelligence, Cardiopulmonary resuscitation, CPR, Deep learning, Transfer learning, 113 Computer and information sciences, 3111 Biomedicine",

author = "Mahmoud Marhamati and Behnam Dorry and Shima Imannezhad and Hussain, {Mohammad Arafat} and Neshat, {Ali Asghar} and Abulfazl Kalmishi and Mohammad Momeny",

note = "Publisher Copyright: {\textcopyright} 2024 The Author(s)",

year = "2024",

month = jul,

doi = "10.1016/j.medengphy.2024.104179",

language = "English",

volume = "129",

journal = "Medical Engineering and Physics",

issn = "1350-4533",

publisher = "ELSEVIER SCI IRELAND LTD",

}

Marhamati, M, Dorry, B, Imannezhad, S, Hussain, MA, Neshat, AA, Kalmishi, A 2024, 'Patient's airway monitoring during cardiopulmonary resuscitation using deep networks', Medical Engineering and Physics, Vuosikerta 129, 104179. https://doi.org/10.1016/j.medengphy.2024.104179

Patient's airway monitoring during cardiopulmonary resuscitation using deep networks. / Marhamati, Mahmoud; Dorry, Behnam; Imannezhad, Shima et al.
julkaisussa: Medical Engineering and Physics, Vuosikerta 129, 104179, 07.2024.

Tutkimustuotos: Artikkelijulkaisu › Artikkeli › Tieteellinen › vertaisarvioitu

TY - JOUR

T1 - Patient's airway monitoring during cardiopulmonary resuscitation using deep networks

AU - Marhamati, Mahmoud

AU - Dorry, Behnam

AU - Imannezhad, Shima

AU - Hussain, Mohammad Arafat

AU - Neshat, Ali Asghar

AU - Kalmishi, Abulfazl

AU - Momeny, Mohammad

N1 - Publisher Copyright:© 2024 The Author(s)

PY - 2024/7

Y1 - 2024/7

N2 - Cardiopulmonary resuscitation (CPR) is a crucial life-saving technique commonly administered to individuals experiencing cardiac arrest. Among the important aspects of CPR is ensuring the correct airway position of the patient, which is typically monitored by human tutors or supervisors. This study aims to utilize deep transfer learning for the detection of the patient's correct and incorrect airway position during cardiopulmonary resuscitation. To address the challenge of identifying the airway position, we curated a dataset consisting of 198 recorded video sequences, each lasting 6–8 s, showcasing both correct and incorrect airway positions during mouth-to-mouth breathing and breathing with an Ambu Bag. We employed six cutting-edge deep networks, namely DarkNet19, EfficientNetB0, GoogleNet, MobileNet-v2, ResNet50, and NasnetMobile. These networks were initially pre-trained on computer vision data and subsequently fine-tuned using the CPR dataset. The validation of the fine-tuned networks in detecting the patient's correct airway position during mouth-to-mouth breathing achieved impressive results, with the best sensitivity (98.8 %), specificity (100 %), and F-measure (97.2 %). Similarly, the detection of the patient's correct airway position during breathing with an Ambu Bag exhibited excellent performance, with the best sensitivity (100 %), specificity (99.8 %), and F-measure (99.7 %).

AB - Cardiopulmonary resuscitation (CPR) is a crucial life-saving technique commonly administered to individuals experiencing cardiac arrest. Among the important aspects of CPR is ensuring the correct airway position of the patient, which is typically monitored by human tutors or supervisors. This study aims to utilize deep transfer learning for the detection of the patient's correct and incorrect airway position during cardiopulmonary resuscitation. To address the challenge of identifying the airway position, we curated a dataset consisting of 198 recorded video sequences, each lasting 6–8 s, showcasing both correct and incorrect airway positions during mouth-to-mouth breathing and breathing with an Ambu Bag. We employed six cutting-edge deep networks, namely DarkNet19, EfficientNetB0, GoogleNet, MobileNet-v2, ResNet50, and NasnetMobile. These networks were initially pre-trained on computer vision data and subsequently fine-tuned using the CPR dataset. The validation of the fine-tuned networks in detecting the patient's correct airway position during mouth-to-mouth breathing achieved impressive results, with the best sensitivity (98.8 %), specificity (100 %), and F-measure (97.2 %). Similarly, the detection of the patient's correct airway position during breathing with an Ambu Bag exhibited excellent performance, with the best sensitivity (100 %), specificity (99.8 %), and F-measure (99.7 %).

KW - Artificial intelligence

KW - Cardiopulmonary resuscitation

KW - CPR

KW - Deep learning

KW - Transfer learning

KW - 113 Computer and information sciences

KW - 3111 Biomedicine

U2 - 10.1016/j.medengphy.2024.104179

DO - 10.1016/j.medengphy.2024.104179

M3 - Article

AN - SCOPUS:85194079562

SN - 1350-4533

VL - 129

JO - Medical Engineering and Physics

JF - Medical Engineering and Physics

M1 - 104179

ER -

Marhamati M, Dorry B, Imannezhad S, Hussain MA, Neshat AA, Kalmishi A et al. Patient's airway monitoring during cardiopulmonary resuscitation using deep networks. Medical Engineering and Physics. 2024 heinä;129:104179. doi: 10.1016/j.medengphy.2024.104179