• 융합정보논문지
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• 제11권4호
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• pp.146-152
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• 2021

본 연구는 그림책을 활용한 시각화 기억훈련 프로그램의 프로토콜을 개발하고 임상에 적용하기 위하여 유용성을 확인하고자 한다. 경도치매 환자 5명을 대상으로 총 5회기의 시각화 기억훈련 프로그램을 진행하였다. 프로그램 진행 전후 단어 기억력, 전반적 기억력검사, 및 우울감검사를 시행하여 그 변화를 관찰하였다. 그 결과 프로그램 시행 후 전반적 기억력의 향상이 있었으나 통계적으로 유의하지 않았다. 반면 우울감에는 통계적으로 유의미한 긍정적인 효과가 있는 것으로 나타났다. 단어 기억력에 있어 일부 대상자는 인출 및 재인의 향상을 보였고 일부 대상자는 변화가 없거나 더 나빠진 것으로 나타났다. 본 프로그램은 일부 경도치매 환자의 기억력 향상에 도움이 되며 우울감이 감소하는 정서적 효과가 있는 것으로 판단된다. 향후 그룹 인지치료 모델로 널리 활용되길 바라며 더 많은 대상자 수의 연구가 필요할 것으로 사료된다.

• 대한의용생체공학회:의공학회지
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• 제42권4호
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• pp.150-158
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• 2021

Intracranial hemorrhage (ICH) refers to acute bleeding inside the intracranial vault. Not only does this devastating disease record a very high mortality rate, but it can also cause serious chronic impairment of sensory, motor, and cognitive functions. Therefore, a prompt and professional diagnosis of the disease is highly critical. Noninvasive brain imaging data are essential for clinicians to efficiently diagnose the locus of brain lesion, volume of bleeding, and subsequent cortical damage, and to take clinical interventions. In particular, computed tomography (CT) images are used most often for the diagnosis of ICH. In order to diagnose ICH through CT images, not only medical specialists with a sufficient number of diagnosis experiences are required, but even when this condition is met, there are many cases where bleeding cannot be successfully detected due to factors such as low signal ratio and artifacts of the image itself. In addition, discrepancies between interpretations or even misinterpretations might exist causing critical clinical consequences. To resolve these clinical problems, we developed a diagnostic model predicting intracranial bleeding and its subtypes (intraparenchymal, intraventricular, subarachnoid, subdural, and epidural) by applying deep learning algorithms to CT images. We also constructed a visualization tool highlighting important regions in a CT image for predicting ICH. Specifically, 1) 27,758 CT brain images from RSNA were pre-processed to minimize the computational load. 2) Three different CNN-based models (ResNet, EfficientNet-B2, and EfficientNet-B7) were trained based on a training image data set. 3) Diagnosis performance of each of the three models was evaluated based on an independent test image data set: As a result of the model comparison, EfficientNet-B7's performance (classification accuracy = 91%) was a way greater than the other models. 4) Finally, based on the result of EfficientNet-B7, we visualized the lesions of internal bleeding using the Grad-CAM. Our research suggests that artificial intelligence-based diagnostic systems can help diagnose and treat brain diseases resolving various problems in clinical situations.