Automatic photoacoustic monitoring of perinatal brain hypoxia with superior sagittal sinus detection

Despite advances in perinatal medicine over decades, perinatal hypoxic-ischemic encephalopathy (HIE) remains a significant cause of fetal cerebral palsy and can lead to other severe medical sequelae or death. Therefore, it is highly desirable to effectively detect brain hypoxia during labor and post...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Journal of biomedical optics Ročník 30; číslo 7; s. 076004
Hlavní autoři: Jiang, Baichuan, Graham, Ernest, Unberath, Mathias, Taylor, Russell H., Koehler, Raymond C., Kang, Jeeun, Boctor, Emad M.
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Society of Photo-Optical Instrumentation Engineers 01.07.2025
Témata:
Algorithms
Animals
Animals, Newborn
Brain - diagnostic imaging
Female
Hypoxia, Brain - diagnostic imaging
Hypoxia-Ischemia, Brain - diagnostic imaging
Image Processing, Computer-Assisted - methods
Neural Networks, Computer
Photoacoustic Techniques - methods
Superior Sagittal Sinus - diagnostic imaging
Swine
brain monitoring
deep learning
perinatal health
photoacoustic imaging
oxygen saturation measurement
ISSN:1083-3668, 1560-2281, 1560-2281
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Despite advances in perinatal medicine over decades, perinatal hypoxic-ischemic encephalopathy (HIE) remains a significant cause of fetal cerebral palsy and can lead to other severe medical sequelae or death. Therefore, it is highly desirable to effectively detect brain hypoxia during labor and postnatally for HIE management. We recently validated the feasibility of transcranial photoacoustic (PA) imaging for oxyhemoglobin saturation measurement at the superior sagittal sinus ( ) in the neonatal piglet brain, at which overall oxygen supply status can be reflected as a primary collective vein. We aim to automate the PA-based workflow of at-risk subject detection and enable fully autonomous and continuous perinatal monitoring. We proposed a two-step algorithm that focuses on the most informative region of the brain for oxygenation status, the superior sagittal sinus (SSS). First, a convolutional neural network (U-Net) is trained to detect the location of SSS in the coronal cross-section PA images. Then, an optimized region of interest patch around the predicted SSS location is cropped from the spectral unmixed image and averaged as the measurement. A confidence score can be computed for the measurement via Monte Carlo dropout (MCD), which infers the prediction uncertainty for better clinical decision-making. The algorithm was evaluated on an piglet brain imaging dataset containing 84 independent experimental settings from 10 piglet subjects. A 10-fold leave-one-subject-out cross-validation experiment reports 85.2% sensitivity and 93.3% specificity for healthy/hypoxia classification with an -squared value of 0.708 and a confidence score of 94.06% based on MCD computation, well agreed with our ground-truth given by blood gas measurements. The proposed automatic monitoring solution demonstrated a hypoxia detection capability comparable to the human expert manual annotation on the same task. We concluded with high feasibility for a noninvasive PA-based continuous monitoring of the perinatal brain.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1083-3668
1560-2281
1560-2281
DOI:10.1117/1.JBO.30.7.076004