Machine learning approach for noninvasive intracranial pressure estimation using pulsatile cranial expansion waveforms
Abstract Noninvasive methods for intracranial pressure (ICP) monitoring have emerged, but none has successfully replaced invasive techniques. This observational study developed and tested a machine learning (ML) model to estimate ICP using waveforms from a cranial extensometer device (brain4care [B4...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-025-01463-y |
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| author | Gustavo Frigieri Sérgio Brasil Danilo Cardim Marek Czosnyka Matheus Ferreira Wellingson S. Paiva Xiao Hu |
| author_facet | Gustavo Frigieri Sérgio Brasil Danilo Cardim Marek Czosnyka Matheus Ferreira Wellingson S. Paiva Xiao Hu |
| author_sort | Gustavo Frigieri |
| collection | DOAJ |
| description | Abstract Noninvasive methods for intracranial pressure (ICP) monitoring have emerged, but none has successfully replaced invasive techniques. This observational study developed and tested a machine learning (ML) model to estimate ICP using waveforms from a cranial extensometer device (brain4care [B4C] System). The model explored multiple waveform parameters to optimize mean ICP estimation. Data from 112 neurocritical patients with acute brain injuries were used, with 92 patients randomly assigned to training and testing, and 20 reserved for independent validation. The ML model achieved a mean absolute error of 3.00 mmHg, with a 95% confidence interval within ±7.5 mmHg. Approximately 72% of estimates from the validation sample were within 0-4 mmHg of invasive ICP values. This proof-of-concept study demonstrates that noninvasive ICP estimation via the B4C System and ML is feasible. Prospective studies are needed to validate the model’s clinical utility across diverse settings. |
| format | Article |
| id | doaj-art-08c26ae2f6194953b6fcf8190a07ad20 |
| institution | Kabale University |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-08c26ae2f6194953b6fcf8190a07ad202025-02-02T12:43:39ZengNature Portfolionpj Digital Medicine2398-63522025-01-018111010.1038/s41746-025-01463-yMachine learning approach for noninvasive intracranial pressure estimation using pulsatile cranial expansion waveformsGustavo Frigieri0Sérgio Brasil1Danilo Cardim2Marek Czosnyka3Matheus Ferreira4Wellingson S. Paiva5Xiao Hu6brain4careDivision of Neurosurgery, Department of Neurology, School of Medicine University of São Paulobrain4careBrain Physics Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge7D AnalyticsDivision of Neurosurgery, Department of Neurology, School of Medicine University of São PauloNell Hodgson Woodruff School of Nursing, Emory UniversityAbstract Noninvasive methods for intracranial pressure (ICP) monitoring have emerged, but none has successfully replaced invasive techniques. This observational study developed and tested a machine learning (ML) model to estimate ICP using waveforms from a cranial extensometer device (brain4care [B4C] System). The model explored multiple waveform parameters to optimize mean ICP estimation. Data from 112 neurocritical patients with acute brain injuries were used, with 92 patients randomly assigned to training and testing, and 20 reserved for independent validation. The ML model achieved a mean absolute error of 3.00 mmHg, with a 95% confidence interval within ±7.5 mmHg. Approximately 72% of estimates from the validation sample were within 0-4 mmHg of invasive ICP values. This proof-of-concept study demonstrates that noninvasive ICP estimation via the B4C System and ML is feasible. Prospective studies are needed to validate the model’s clinical utility across diverse settings.https://doi.org/10.1038/s41746-025-01463-y |
| spellingShingle | Gustavo Frigieri Sérgio Brasil Danilo Cardim Marek Czosnyka Matheus Ferreira Wellingson S. Paiva Xiao Hu Machine learning approach for noninvasive intracranial pressure estimation using pulsatile cranial expansion waveforms npj Digital Medicine |
| title | Machine learning approach for noninvasive intracranial pressure estimation using pulsatile cranial expansion waveforms |
| title_full | Machine learning approach for noninvasive intracranial pressure estimation using pulsatile cranial expansion waveforms |
| title_fullStr | Machine learning approach for noninvasive intracranial pressure estimation using pulsatile cranial expansion waveforms |
| title_full_unstemmed | Machine learning approach for noninvasive intracranial pressure estimation using pulsatile cranial expansion waveforms |
| title_short | Machine learning approach for noninvasive intracranial pressure estimation using pulsatile cranial expansion waveforms |
| title_sort | machine learning approach for noninvasive intracranial pressure estimation using pulsatile cranial expansion waveforms |
| url | https://doi.org/10.1038/s41746-025-01463-y |
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