Deep Learning Algorithm Helps Detect Cerebral Aneurysms

December 3, 2018

Radiologic reports with deep learning algorithm provide improved cerebral aneurysm detection.

A deep learning algorithm can detect cerebral aneurysms in radiologic reports with high sensitivity, according to a study published in the journal Radiology

Researchers from Japan sought to develop and evaluate a supportive algorithm using deep learning for detecting cerebral aneurysms at time-of-flight MR angiography to provide a second assessment of images already interpreted by radiologists.

The researchers extracted MR images reported by radiologists that contained aneurysms for the period from November 2006 through October 2017. The images were divided into three data sets:

  • Training data set
  • Internal test data set
  • External test data set

They then constructed the algorithm by deep learning with the training data set, and its sensitivity to detect aneurysms in the test data sets was evaluated. To find aneurysms that had been overlooked in the initial reports, two radiologists independently performed a blinded interpretation of aneurysm candidates detected by the algorithm. When there was disagreement, the final diagnosis was made in consensus. The number of newly detected aneurysms was also evaluated.

The results showed that the training data set, which provided training and validation data, included 748 aneurysms (mean size, 3.1 mm ± 2.0) from 683 examinations; 318 of these examinations were on male patients (mean age, 63 years ± 13) and 365 were on female patients (mean age, 64 years ± 13).

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Test data were provided by the internal test data set (649 aneurysms [mean size, 4.1 mm ± 3.2] in 521 examinations, including 177 male patients and 344 female patients with mean age of 66 years ± 12 and 67 years ± 13, respectively) and the external test data set (80 aneurysms [mean size, 4.1 mm ± 2.1] in 67 examinations, including 19 male patients and 48 female patients with mean age of 63 years ± 12 and 68 years ± 12, respectively). The sensitivity for the internal test data sets was 91% (592 of 649) and 93% (74 of 80) for the external test data sets. The algorithm improved aneurysm detection in the internal and external test data sets by 4.8% (31 of 649) and 13% (10 of 80), respectively, compared with the initial reports.

The researchers concluded that a deep learning algorithm detected cerebral aneurysms in radiologic reports with high sensitivity and improved aneurysm detection compared with the initial reports.