Keywords
Diagnostic ultrasound image; Mechanical index; Thermal index; Transducer frequency; Optical character recognition; Matching pattern; Computerized algorithm
Abstract
Background: Diagnostic Ultrasound (dUS) images capture acoustic measures. Higher level of acoustic measures may increase the likelihood of adverse neurodevelopment.
Objective: To develop a computerized algorithm to extract acoustic measures from dUS images.
Method: The dUS images of 484 pregnant women in 2014 were extracted from the Electronic Medical Record (EMR) system within an integrated healthcare organization. The retrieved dUS images were processed by the optical character recognition engine, Tesseract, to recognize the embedded texts. A set of matching patterns was constructed to extract the values associated with Thermal Index (TI), Mechanical Index (MI) and transducer frequency from these recognized texts. A sample of 200 randomly selected dUS images was processed by the computerized algorithm and results were compared against the gold standard of perinatal expert reviews.
Results: 54,909 dUS images were extracted from the EMR system. 52,637 of them had at least one of acoustic measures. The mean of extracted TI, MI and transducer frequency were 1.05, 1.08 and 4.34(MHz), respectively. Higher frequencies of dUS (5-7 MHz), higher MI (≥1.00) and higher TI (≥1.00) were used during first trimester, first/second trimesters and second/third trimesters, respectively. The computerized algorithm achieved a performance with sensitivity of 99.0%, 93.3%, 62.0% and positive predictive value of 100.0%, 99.5%, 95.8% for TI, MI and transducer frequency, respectively.
Conclusions: Our study successfully developed a computerized algorithm to extract TI, MI and transducer frequency from dUS images. Implementation of this algorithm can provide values for examining potential effects of acoustic measures on perinatal outcomes and evidence-based decision making.
Citation
Xie F, Sandor C, Lewis DA and Getahun D. A Computerized Algorithm of Extracting Acoustic Measures from Prenatal Diagnostic Ultrasound Images. SM J Public Health Epidemiol. 2018; 4(1): 1046s1.