An innovative ointment solves an unmet clinical need to cure keloid wounds The composition of the ointment can precisely modulate the cell functionsgene expression of keloid fibroblasts, making them close to the performance of normal fibroblasts. Thereby, when applying the ointment to keloid woundsthe human body prone to the formation of keloid wounds, the wound is smoothed, healing close to normal skin.

"The primary goal of this project is to establish a complete hospital-based liver cancer database, profiles for data feature extraction,develop different cancer, prediction models. A Medical AI program to predict the poster treatment (including operationradiofrequency ablation) recurrence of liver cancer will be established. The program system will assist doctors in the medical decision, identify high-risk patients,adjust clinical follow-up programs."

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.