Quantification of respiratory effort magnitude in spontaneous breathing patients using Convolutional Autoencoders

Ang, Christopher Yew Shuen; Chiew, Yeong Shiong; Vu, Lien Hong; MBChB, Matthew E Cove

doi:10.1016/j.cmpb.2021.106601

Cited by 10 publications

(2 citation statements)

References 46 publications

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“…Therefore, the continuous assessment of SB is necessary. In a study (24), a convolutional autoencoder (CAE) was developed to quantify the amount of SB using airway pressure and flow waveform data. The characteristics of each reviewed study in this category are summarized in Table 4.…”

Section: Category C: Control Of Mechanical Ventilation and Weaningmentioning

confidence: 99%

Machine Learning-Guided Anesthesiology: A Review of Recent Advances and Clinical Applications

Hashemi,

Yousefzadeh,

Abin

et al. 2024

J Cell Mol Anesth

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: Anesthesia is the process of inducing and experiencing various conditions, such as painlessness, immobility, and amnesia, to facilitate surgeries and other medical procedures. During the administration of anesthesia, anesthesiologists face critical decision-making moments, considering the significance of the procedure and potential complications resulting from anesthesia-related choices. In recent years, artificial intelligence (AI) has emerged as a supportive tool for anesthesia decisions, given its potential to assist with control and management tasks. This study aims to conduct a comprehensive review of articles on the intersection of AI and anesthesia. A review was conducted by searching PubMed for peer-reviewed articles published between 2020 and early 2022, using keywords related to anesthesia and AI. The articles were categorized into nine distinct groups: "Depth of anesthesia", "Control of anesthesia delivery", "Control of mechanical ventilation and weaning", "Event prediction", "Ultrasound guidance", "Pain management", "Operating room logistic", "Monitoring", and "Neuro-critical care". Four reviewers meticulously examined the selected articles to extract relevant information. The studies within each category were reviewed by considering items such as the purpose and type of anesthesia, AI algorithms, dataset, data accessibility, and evaluation criteria. To enhance clarity, each category was analyzed with a higher resolution than previous review articles, providing readers with key points, limitations, and potential areas for future research to facilitate a better understanding of each concept. The advancements in AI techniques hold promise in significantly enhancing anesthesia practices and improving the overall experience for anesthesiologists.

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Section: Category C: Control Of Mechanical Ventilation and Weaningmentioning

confidence: 99%

Machine Learning-Guided Anesthesiology: A Review of Recent Advances and Clinical Applications

Hashemi,

Yousefzadeh,

Abin

et al. 2024

J Cell Mol Anesth

View full text Add to dashboard Cite

show abstract

“…In particular, this data can be further processed to provide respiratory mechanics and other ventilatory information not available on today’s ventilators, but useful to personalise and guide MV treatment [12] , [13] , [14] , [15] , [16] . CAREDAQ thus provides a platform for future development and integration of software modules, including machine learning models [17] , [18] , [19] or model-based algorithms [14] , [16] , [20] , [21] which could potentially provide real-time, proactive, and patient-specific MV decision support and care, improving care and outcomes.…”

Section: Hardware In Contextmentioning

confidence: 99%