In view of the problem that there is no standard diagnosis for early stage keratoconus disease,at the same time to assist the special examiner and ophthalmologist to make the early diagnosis effectively,the advantages and disadvantages of each testing instrument were analyzed.In order to construct an assistant system for early diagnosis of keratoconus,a deep learning technique was applied in corneal OCT examination.The system used improved VGG-16 to realize the recognition accuracy of about 68% keratoconus keratopathy,and the clinical results showed that the system can help doctors to give diagnosis confidence to a certain extent.At the same time,the physician's re-marking of OCT can help train the system for more accurate judgment.
Corneal Topography ; Deep Learning ; Early Diagnosis ; Humans ; Keratoconus ; diagnostic imaging ; Tomography, Optical Coherence

Through analyzing the existing problems in the current mode, the vital signs monitoring information system based on cloud platform is designed and developed. The system's aim is to assist nurse carry out vital signs nursing work effectively and accurately. The system collects, uploads and analyzes patient's vital signs data by PDA which connecting medical inspection equipments. Clinical application proved that the system can effectively improve the quality and efficiency of medical care and may reduce medical expenses. It is alse an important practice result to build a medical cloud platform.
Cloud Computing ; Humans ; Monitoring, Physiologic ; instrumentation ; Vital Signs