Automated ASPECTS Segmentation and Scoring Tool: a Method Tailored for a Colombian Telestroke Network.
Esteban OrtizJuan RiveraManuel Felipe GranjaNelson AgudeloMarcela Hernández HoyosAntonio José SalazarPublished in: Journal of imaging informatics in medicine (2024)
To evaluate our two non-machine learning (non-ML)-based algorithmic approaches for detecting early ischemic infarcts on brain CT images of patients with acute ischemic stroke symptoms, tailored to our local population, to be incorporated in our telestroke software. One-hundred and thirteen acute stroke patients, excluding hemorrhagic, subacute, and chronic patients, with accessible brain CT images were divided into calibration and test sets. The gold standard was determined through consensus among three neuroradiologist. Four neuroradiologist independently reported Alberta Stroke Program Early CT Scores (ASPECTSs). ASPECTSs were also obtained using a commercial ML solution (CMLS), and our two methods, namely the Mean Hounsfield Unit (HU) relative difference (RELDIF) and the density distribution equivalence test (DDET), which used statistical analyze the of the HUs of each region and its contralateral side. Automated segmentation was perfect for cortical regions, while minimal adjustment was required for basal ganglia regions. For dichotomized-ASPECTSs (ASPECTS < 6) in the test set, the area under the receiver operating characteristic curve (AUC) was 0.85 for the DDET method, 0.84 for the RELDIF approach, 0.64 for the CMLS, and ranged from 0.71-0.89 for the neuroradiologist. The accuracy was 0.85 for the DDET method, 0.88 for the RELDIF approach, and was ranged from 0.83 - 0.96 for the neuroradiologist. Equivalence at a margin of 5% was documented among the DDET, RELDIF, and gold standard on mean ASPECTSs. Noninferiority tests of the AUC and accuracy of infarct detection revealed similarities between both DDET and RELDIF, and the CMLS, and with at least one neuroradiologist. The alignment of our methods with the evaluations of neuroradiologist and the CMLS indicates the potential of our methods to serve as supportive tools in clinical settings, facilitating prompt and accurate stroke diagnosis, especially in health care settings, such as Colombia, where neuroradiologist are limited.
Keyphrases
- deep learning
- machine learning
- convolutional neural network
- acute ischemic stroke
- healthcare
- dual energy
- image quality
- computed tomography
- artificial intelligence
- contrast enhanced
- cerebral ischemia
- atrial fibrillation
- positron emission tomography
- high throughput
- white matter
- magnetic resonance imaging
- resting state
- liver failure
- optical coherence tomography
- high resolution
- big data
- smoking cessation
- heart failure
- blood brain barrier
- subarachnoid hemorrhage
- quality improvement
- hepatitis b virus
- left ventricular
- clinical practice
- percutaneous coronary intervention
- acute coronary syndrome
- oxidative stress
- data analysis
- depressive symptoms
- label free
- social media
- quantum dots
- brain injury
- mechanical ventilation
- single cell