Optimization of clinical risk-factor interpretation and radiological findings with machine learning for PIRADS category 3 patients
Background: Due to the low cancer-detection rate in patients with PIRADS category 3 lesions, we created machine learning (ML) models to facilitate decision-making about whether to perform prostate biopsies or monitor clinical information without biopsy results. Methods: In our retrospective, single-...
Saved in:
Main Author: | Aussavavirojekul P. |
---|---|
Other Authors: | Mahidol University |
Format: | Article |
Published: |
2023
|
Subjects: | |
Online Access: | https://repository.li.mahidol.ac.th/handle/123456789/86179 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Mahidol University |
Similar Items
-
Optimization of clinical risk-factor interpretation and radiological findings with machine learning for PIRADS category 3 patients
by: Pubordee Aussavavirojekul, et al.
Published: (2022) -
Interpretable machine learning for optimizing computer system
by: Chen, Peilin
Published: (2024) -
The radiological findings in syphilitic proctitis
by: Singcharoen T., et al.
Published: (2014) -
Radiological findings in 31 patients with chondroblastoma in tubular and non-tubular bones
by: Suphaneewan Jaovisidha, et al.
Published: (2018) -
Neoplastic meningitis: A retrospective review of clinical presentations, radiological and cerebrospinal fluid findings
by: Sorrawit Jearanaisilp, et al.
Published: (2018)