With the integration of AI methods into the clinical workflow, you can perform reproducible and accurate radiology evaluations. It works like an excellent tool that helps in assisting Radiologists Service in European countrieslike Netherlands.
AI methods used in radiology-based clinical application areas
Thoracic Imaging:
One of the deadly and common tumours is lung cancer. Through screening, the pulmonary nodules can be identified. When detected at an early stage can save the lives of patients. AI helps to automatically identify the nodules and segments them as malignant or benign.- Pelvic & Abdominal Imaging: Magnetic Resonance Imaging (MRI) and Computer Tomography (CT) helps to identify findings that may include liver lesions. The implementation of AI categorises these lesions and helps prioritizes follow-ups for patients.
- Colonoscopy: When colonic polyps are left undetected, it leads to major risks related to colorectal cancer. Polyps are benign at the initial stage but can become malignant. Therefore, with AI tools, early detection and monitoring can be done.
- Mammography: interpreting the results is technically a bit too challenging when it comes to mammography screening. The robust AI-based tools assist by interpreting as it identifies and segments microcalcifications.
- Brain Imaging: The unusual tissue growth in the brain can often lead to brain tumours. AI helps to identify the right diagnosis based on the severity of the issue.
- Radiation Oncology: The treatment of radiation can be automated by segregating tumours for increasing the radiation dose. AI assists by performing assessments with speed and accuracy to make the radiation therapy work successfully.
References:
1. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6268174/
2. https://www.aidoc.com/blog/artificial-intelligence-in-radiology/