Artificial intelligence in technologies for segmentation and classification of neuro-oncological lesions
The study is devoted to considering the effectiveness of modern approaches to the development of diagnostic technology for analyzing MRI images in neuro-oncology, based on artificial intelligence (AI) and computer vision. Such approaches are necessary for rapid and diagnostically effective analysis...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | Russian |
| Published: |
Russian Academy of Sciences, Siberian Branch Publishing House
2024-03-01
|
| Series: | Сибирский научный медицинский журнал |
| Subjects: | |
| Online Access: | https://sibmed.elpub.ru/jour/article/view/1387 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | The study is devoted to considering the effectiveness of modern approaches to the development of diagnostic technology for analyzing MRI images in neuro-oncology, based on artificial intelligence (AI) and computer vision. Such approaches are necessary for rapid and diagnostically effective analysis to implement the principle of individualized medicine. Material and methods. An analysis of the effectiveness of the choice of AI technologies for the formation of processes of segmentation and classification of neuro-oncological MRI images has been presented. AI was trained on its own annotated database (SBT Dataset), containing about 1000 clinical cases based on archival data from preoperative MRI studies at the Federal Neurosurgical Center (Novosibirsk, Russian Federation), in patients with astrocytoma, glioblastoma, meningioma, neuroma, and with metastases of somatic tumors, with histological and histochemical postoperative confirmation. Results and discussion. The effectiveness and efficiency of the developed technologies was tested during the international BraTS competition, in which it was proposed to segment and classify cases from a dataset of neuro-oncological patients prepared by the competition organizers. Conclusions. The methodological approaches proposed in the article in the development of diagnostic systems based on AI and the principles of computer vision have shown high efficiency at the level of dozens of world leaders and can be used to develop software and hardware systems for diagnostic neuroradiology with the functions of a “doctor’s assistant.” |
|---|---|
| ISSN: | 2410-2512 2410-2520 |