Automated ASPECTS Segmentation and Scoring Tool: a Method Tailored for a Colombian Telestroke Network.

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 str...

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Publicado en:Journal of Imaging Informatics in Medicine Vol. 38; no. 2; pp. 1076 - 1091
Autores principales: Ortiz, Esteban, Rivera, Juan, Granja, Manuel, Agudelo, Nelson, Hernández Hoyos, Marcela, Salazar, Antonio
Formato: diagnostic images research tables/charts Journal Article
Publicado: Springer Nature Apr2025
Acceso en línea:Ver este registro en EBSCOhost
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      pub: Springer Nature
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        atl: Automated ASPECTS Segmentation and Scoring Tool: a Method Tailored for a Colombian Telestroke Network.
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          Ortiz, Esteban
          Rivera, Juan
          Granja, Manuel
          Agudelo, Nelson
          Hernández Hoyos, Marcela
          Salazar, Antonio
        affil: https://ror.org/02mhbdp94 Systems and Computing Engineering Department, Universidad de los Andes, Bogotá, Colombia
      sug:
        subj:
          Ischemic Stroke Radiography
          Brain Radiography
          Telemedicine
          Tomography, X-Ray Computed Methods
          Automation
          Prediction Algorithms
          Instrument Construction
          Instrument Validation
          Colombia
          Funding Source
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          Aged, 80 and Over
          ROC Curve
          Cerebrovascular Disorders
          Emergency Medicine
          Thrombolytic Therapy
          Sensitivity and Specificity
          kappa Statistic
          Data Analysis Software
          Descriptive Statistics
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      ab: 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.
      pubtype: Academic Journal
      doctype:
        diagnostic images
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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