## 3 Discussion
The core technology involved Convolutional Neural Network (CNN) for registration, segmentation, lesion detection and characterization, and prognosis. Generative Adversarial Nets (GAN) Image augmentation for learning, Linear Regression, Random Forest, and Gradient Boosting Machine are included for prognosis. In addition, we will conduct radiomics for analysis, including deep multimodal feature analysis. We can provide decoding services for PCa MRI, biopsy guides for PCa MRI/fusion, and prediction services for PCa. VUNO Inc. has contributed to the development of the second SW. VUNO Inc. has performed many projects in deep learning in medical image analysis [17; 18; 19; 20]. The last SW demarcates the cancer area and calculates the Gleason's score from specimen scan images to designate the risk level of certain tissues from specimen scan images. This SW will use deep learning to classify patches according to Gleason pattern 3, 4, 5, etc. In addition, the SW will use weakly supervised learning and classic machine learning to predict PCa and Gleason score using results and characteristics of the patch. This SW is a support system for PCa diagnosis and is able to screen normal slides or notice risky areas before reading the specimen. It is possible to extend the SW using imaging data to obtain precise results. DeepBio Inc. plays a role in the development of this SW. DeepBio has conducted AI projects in both pathology and PCa [21]. The PROMISE CLIP study has plans to validate four intelligent SW both internally and externally. We are going to obtain additional large-scale medical data from four hospitals for a duration of two years, including clinical, imaging, and pathology data. There are a few limitations of this study. First, we collected EMR data retrospectively. The PROMISE CLIP registry was unable to include all patient data. However, PROMISE CLIP has the potential to use diverse AI projects to tackle unsolved medical demands in PCa. Second, the PROMISE CLIP registry is for the Korean population. Future projects will need to collect multinational data. Third, we integrated EMR data from four hospitals to develop the registry. Future projects will need to collect addition data from diverse hospitals. Although there are limitations, the PROMISE CLIP project will direct guidelines for intelligent SW development to solve challenging medical demands in PCa. The PROMISE CLIP registry plays an important role in advancing PCa research and care. [PERSON] wrote the article and contributed in building the project. [PERSON] also supported in writing the article and building the project. [PERSON] gave medical advices and developing the standard of database of the proposed system. [PERSON], [PERSON], [PERSON] gave medical advices and building the pathology database of the system. [PERSON] and [PERSON] gave medical advices and building the clinical database of the system. [PERSON] gave medical advices and building the image database of the system. [PERSON], [PERSON]-S.K., and [PERSON] supervised research and the project. [PERSON], [PERSON]-S.K., and [PERSON] also gave medical advices for building the project. All twelve authors substantially contributed in each of their expertise. This work was supported by the Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korean government (MSIT) (2018-2-00861, Intelligent SW Technology Development for Medical Data Analysis).This research was supported by the Bio & Medical Technology Development Program of the National Research Foundation (NRF) funded by the Korean government (MSIT) (NRF-2017M3A9B8069577). Thank you [PERSON] at department of pathology, Asan Medical Center for the contribution for building the pathology database of the system. Thank you all of each hospital members who contributed their effort for the project. There are no competing financial interests. The authors declare no conflict of interest.