Acute and Critical Care

Original Article

Neurosurgery
Deep learning-based model for detection of intracranial waveforms with poor brain compliance in southern Thailand: Thara Tunthanathip, Avika Trakulpanitkit; Acute Crit Care. 2025;40(3):473-481. Published online August 29, 2025; DOI: https://doi.org/10.4266/acc.001425

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Background
Intracranial pressure (ICP) waveform analysis provides critical insights into brain compliance and can aid in the early detection of neurological deterioration. Deep learning (DL) has recently emerged as an effective approach for analyzing complex medical signals and imaging data. The aim of the present research was to develop a DL-based model for detecting ICP waveforms indicative of poor brain compliance. Methods: A retrospective cohort study was conducted using ICP wave images collected from postoperative hydrocephalus (HCP) patients who underwent ventriculostomy. The images were categorized into normal and poor compliance waveforms. Precision, recall, mean average precision at the 0.5 intersection over union (mAP_0.5), and the area under the receiver operating characteristic curve (AUC) were used to test. Results: The dataset consisted of 2,744 ICP wave images from 21 HCP patients. The best-performing model achieved a precision of 0.97, a recall of 0.96, and a mAP_0.5 of 0.989. The confusion matrix for poor brain compliance waveform detection using the test dataset also demonstrated a high classification accuracy, with true positive and true negative rates of 48.5% and 47.8%, respectively. Additionally, the model demonstrated high accuracy, achieving a mAP_0.5 of 0.994, sensitivity of 0.956, specificity of 0.970, and an AUC of 0.96 in the detection of poor compliance waveforms. Conclusions: The DL-based model successfully detected pathological ICP waveforms, thereby enhancing clinical decision-making. As DL advances, its significance in neurocritical care will help to pave the way for more individualized and data-driven approaches to brain monitoring and management

Citations

Citations to this article as recorded by

Perioperative Anesthetic Strategies in Emergent Neurosurgery During Severe Traumatic Brain Injury
Denise Baloi, Clayton Rawson, Deondra Montgomery, Michael Karsy, Mehrdad Pahlevani
Trauma Care.2026; 6(1): 5. CrossRef

Review Article

Pulmonary
Asynchronies during invasive mechanical ventilation: narrative review and update: Santiago Nicolás Saavedra, Patrick Valentino Sepúlveda Barisich, José Benito Parra Maldonado, Romina Belén Lumini, Alberto Gómez-González, Adrián Gallardo; Acute Crit Care. 2022;37(4):491-501. Published online November 30, 2022; DOI: https://doi.org/10.4266/acc.2022.01158

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Abstract PDF Supplementary Material

Invasive mechanical ventilation is a frequent therapy in critically ill patients in critical care units. To achieve favorable outcomes, patient and ventilator interaction must be adequate. However, many clinical situations could attempt against this principle and generate a mismatch between these two actors. These asynchronies can lead the patient to worst outcomes; that is why it is vital to recognize and treat these entities as soon as possible. Early detection and recognition of the different asynchronies could favor the reduction of the days of mechanical ventilation, the days of hospital stay, and intensive care and improve clinical results.

Citations

Citations to this article as recorded by

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