PCA-BM includes two models: “Automatic BMs Segmentation AI Model”" Distant Brain Failure Prediction Model". The former one uses C2FNAS (coarse to fine network architecture search) to detect the location, size,number of brain metastases. The latter uses radiomics to extract numerous radiographic featuresemploys machine learning methods such as XGBoost to establish a prognostic model of brain metastases. PCA-BM provides more precision treatment decisions for patientsimproves personalizedaccurate overall stereotactic radiosurgery planning.

PCA-BM implements AI technology in clinical applications. In addition to the deep learning technology used in most tumor segmentation models, it also includes an innovative automatic labeling system for brain metastasessurrounding critical organs. The vital organs are outlined together to assist doctors in making more accurate interpretationstreatment decisions. In addition, it provides side-effect risk assessment of stereotactic radiosurgeryrelated prognosis prediction, which has made

The target audience of PCA-BM is physical examination centersradiology departments at healthcare facilities. By integrating softwarehardware, the diagnosis time can be shortened due to the prompt interpretation of MRI,the postoperative recovery can be predicted. It can also help to formulate a personalized treatment plan to improve the efficiencyquality of treatment. PCA-BM can also be integrated with telemedicine through remote devices, so that interpretation results can be provided in various medical domains timely,accelerate the diagnosisreferral process.

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