{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T11:56:14Z","timestamp":1784116574341,"version":"3.55.0"},"reference-count":43,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,11,23]],"date-time":"2024-11-23T00:00:00Z","timestamp":1732320000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Entrepreneurship and Innovation Support Program for Overseas Students in Chongqing","award":["cx2021102"],"award-info":[{"award-number":["cx2021102"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Brain Sciences"],"abstract":"<jats:p>Objectives: Cerebral edema, a prevalent consequence of brain injury, is associated with significant mortality and disability. Timely diagnosis and monitoring are crucial for patient prognosis. There is a pressing clinical demand for a real-time, non-invasive cerebral edema monitoring method. Ultrasound methods are prime candidates for such investigations due to their non-invasive nature. Methods: Acute cerebral edema was introduced in rats by permanently occluding the left middle cerebral artery (MCA). Ultrasonic echo signals were collected at nine time points over a 24 h period to extract features from both the time and frequency domains. Concurrently, histomorphological changes were examined. We utilized support vector machine (SVM), logistic regression (LogR), decision tree (DT), and random forest (RF) algorithms for classifying cerebral edema types, and SVM, RF, linear regression (LR), and feedforward neural network (FNNs) for predicting the cerebral infarction volume ratio. Results: The integration of 16 ultrasonic features associated with cerebral edema development with the RF model enabled effective classification of cerebral edema types, with a high accuracy rate of 97.9%. Additionally, it provided an accurate prediction of the cerebral infarction volume ratio, with an R2 value of 0.8814. Conclusions: Our proposed strategy classifies cerebral edema and predicts the cerebral infarction volume ratio with satisfactory precision. The fusion of ultrasound echo features with machine learning presents a promising non-invasive approach for the monitoring of cerebral edema.<\/jats:p>","DOI":"10.3390\/brainsci14121175","type":"journal-article","created":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T03:59:01Z","timestamp":1732507141000},"page":"1175","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Non-Invasive Monitoring of Cerebral Edema Using Ultrasonic Echo Signal Features and Machine Learning"],"prefix":"10.3390","volume":"14","author":[{"given":"Shuang","family":"Yang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Ultrasound in Medicine and Engineering, Chongqing Medical University, Chongqing 400016, China"},{"name":"Chongqing Key Laboratory of Biomedical Engineering, Chongqing Medical University, Chongqing 400016, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanbo","family":"Yang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Ultrasound in Medicine and Engineering, Chongqing Medical University, Chongqing 400016, China"},{"name":"Chongqing Key Laboratory of Biomedical Engineering, Chongqing Medical University, Chongqing 400016, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4086-2150","authenticated-orcid":false,"given":"Yufeng","family":"Zhou","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Ultrasound in Medicine and Engineering, Chongqing Medical University, Chongqing 400016, China"},{"name":"Chongqing Key Laboratory of Biomedical Engineering, Chongqing Medical University, Chongqing 400016, China"},{"name":"National Medical Products Administration (NMPA), Key Laboratory for Quality Evaluation, Ultrasonic Surgical Equipment, 507 Gaoxin Ave., Donghu New Technology Development Zone, Wuhan 430075, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1172","DOI":"10.1212\/CON.0000000000000988","article-title":"Management of cerebral edema, brain compression, and intracranial pressure","volume":"27","author":"Liotta","year":"2021","journal-title":"CONTINUUM Lifelong Learn. 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