DISCUSSION
mass‐forming chronic, cancer, serous cystadenoma, hemangioma, malignant tumors, pancreatic cancer, pancreatitis
CANCER, SEROUS CYSTADENOMA, METASTASES, CHRONIC PANCREATITIS, HEMANGIOMA, MALIGNANT TUMORS, PANCREATIC CANCER, LYMPHOMA, NEUROENDOCRINE TUMORS, DESMOPLASIA
Identification and differentiation of pancreatic cancer and mass‐forming chronic pancreatitis is critical and remains a significant problem. Although new methods including EUS, CH‐EUS, and EUS‐FNA have dramatically improved the diagnostic yield of pancreatic masses,An emerging body of work demonstrates that deep learning algorithms such as neural network are capable of learning complex features and assisting in diagnosis. In regard to the focus of this study, several different neural networks have been used. For EUS, Das et al. used an artificial neural network (ANN) model to distinguish pancreatic cancer, chronic pancreatitis, and normal pancreatic tissue by digital analysis of EUS images and obtained 93% sensitivity.This study is, to the best of our knowledge, the first attempt to develop a real‐time image analysis AI system for the purpose of diagnosing pancreatic masses. This system is especially useful because it can be loaded onto standard CH‐EUS equipment and used to guide EUS‐FNA. The hardest part of Model 1 establish was real‐time image tracking. When Model 1 was integrated into the system, it affected the recognition and segmentation results of pancreatic masses due to image jitter and displacement caused by the patient's breathing and heartbeat. In order to solve this problem, we built a video image stabilization technology based on feature point matching to perform real‐time correction and registration of ultrasound images to prevent real‐time changes from affecting path planning. First, multiple frames of video images and image corners (feature points) were obtained by sequential execution. Second, the optical flow method was used to track the corners. Finally, the affine change matrix representing the motion was obtained according to the changes of the corners of the two images before and after. According to the affine change matrix, the trajectory was calculated and smoothed, then the affine change matrix after smooth motion was obtained, resulting in a stable image. Simply put, this technology ensures the consistency of the results of the previous and subsequent frames when Model 1 performs pancreatic mass recognition and segmentation.To differentiate pancreatic cancer from mass‐forming chronic pancreatitis, CH‐EUS MASTER performs TIC analysis and gives a confidence value in which values >0.5 are classified as cancerous. Many studies have found that parameters of TIC are useful in the differential diagnosis of focal lesions in pancreatic cancer and chronic pancreatitis. This can be explained by different perfusion patterns at a capillary level in different types of lesions. Due to low vascular density and desmoplasia, pancreatic cancer is typically hypoenhanced compared with the adjacent pancreatic tissue in all phases, while chronic pancreatitis is isoenhanced or hyperenhanced.Although EUS‐FNA is currently the gold standard for PC diagnosis, its negative predictive value (NPV) for cancer diagnosis is still insufficient, and the average ratio of the largest series is 70%.This study had several limitations. First, the sample size was small, though we used data augmentation techniques, which guaranteed the training effect of AI with small sample size. Second, patients with other types of focal pancreatic masses (either benign masses like serous cystadenoma or hemangioma as well as malignant tumors such as neuroendocrine tumors, pancreatic metastases, lymphoma, or other rare pancreatic masses) were excluded from the analysis because of the low number of cases. However, multicenter and large sample studies are now underway.In conclusion, CH‐EUS MASTER can be a promising system for objectively diagnosing malignant and benign pancreatic masses in real time and may guide FNA in real time.