A revolutionary acute subdural hematoma detection based on two-tiered artificial intelligence model İki katmanlı yapay zeka modeline dayalı devrimsel akut subdural hematom tespiti


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Kaya İ., GENÇTÜRK T. H., KAYA GÜLAĞIZ F.

Ulusal Travma ve Acil Cerrahi Dergisi, vol.29, no.8, pp.858-871, 2023 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 29 Issue: 8
  • Publication Date: 2023
  • Doi Number: 10.14744/tjtes.2023.76756
  • Journal Name: Ulusal Travma ve Acil Cerrahi Dergisi
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, CINAHL, MEDLINE
  • Page Numbers: pp.858-871
  • Keywords: Acute subdural hematoma, artificial intelligence, early diagnosis
  • Kocaeli University Affiliated: Yes

Abstract

BACKGROUND: The article was planned to make the first evaluation in terms of acute subdural hemorrhages, thinking that it can help in appropriate pathologies by tomography interpretation with the artificial intelligence (AI) method, at least in a way to quickly warn the responsible doctor. METHODS: A two-level AI-based hybrid method was developed. The proposed model uses the mask-region convolutional neural network (Mask R-CNN) technique, which is a deep learning model, in the hemorrhagic region’s mask generation stage, and a problem-specific, optimized support vector machines (SVM) technique which is a machine learning model in the binary classification stage. Furthermore, the bee colony algorithm was used for the optimization of SVM algorithms’ parameters. RESULTS: In the first stage, the mean average precision (mAP) value was obtained as 0.754 when the intercept over union (IOU) value was taken as 0.5 with the Mask R-CNN architecture used. At the same time, when a 5-fold cross-validation was applied, the mAP value was obtained 0.736. With the hyperparameter optimization for both Mask R-CNN and the SVM algorithm, the accuracy of the two-level classification process was obtained as 96.36%. Furthermore, final false-negative rate and false-positive rate values were obtained as 6.20%, and 2.57%, respectively. CONCLUSION: With the proposed model, both the detection of hemorrhage and the presentation of the suspicious area to the physician were performed more successfully on two dimensional (2D) images with low cost and high accuracy compared to similar studies and today’s interpretations with telemedicine techniques.