ABSTRACT
Objective To study the efficiency and accuracy of artificial intelligence (AI) system based on fundus photograph in diabetic retinopathy(DR)screening,and evaluate the clinical application value of AI system. Methods A diagnostic trial was adopted. Total of 13683 color fundus photos were collected in Zhaoqing Gaoyao People's Hospital from March,2017 to November,2018. The AI system for DR (ZOC-DR-V1) was established,based on transfer learning + NASNet algorithm,by training 4465 precisely labeled fundus images (2510 normal,and 1955 with any stage of DR). One thousand confirmed fundus images (300 normal and 700 with any stage of DR),diagnosed by AI ( AI group ) and doctors ( 3 ophthalmologist doctors and 3 endocrinologist doctors ) ( doctor group ) , respectively. Ophthalmologist group and endocrinologist group were both composed of primary,intermediate and senior physicians. The mean reading time of each image and the total time of 1000 images were recorded. The accuracy and efficiency of AI system and doctor groups were compared. The reading process was divided into two stages. The diagnostic coincidence rate and the average reading time of each group between the two parts were calculated and compared. This study protocol was approved by Ethic Committee of Zhongshan Ophthalmic Center, Sun Yat-sen University (No. 2017KYPJ104). Results After training,the diagnostic coincidence rate of AI system (ZOC-DR-V1) in test set was 94. 7％,AUC was 0. 994. In this "man-machine to war",the diagnostic coincidence rate of primary,intermediate and senior endocrinologist was 94. 0％,91. 4％ and 93. 4％;the diagnostic coincidence rate of primary,intermediate and senior ophthalmologist was 92. 7％,94. 4％ and 95. 6％;the diagnostic coincidence rate of AI system was 95. 2％. There was no difference in the diagnostic coincidence rate between AI system and senior ophthalmologist ( P = 0. 749 ) . The mean reading time of each image of primary, intermediate and senior endocrinologists was (4. 63±1. 87),(3. 74±3. 47) and (5. 71±3. 47) seconds,and the total time of 1000 images of primary,intermediate and senior endocrinologists was 1. 29,1. 04 and 1. 58 hours;the mean reading time of each image of primary,intermediate and senior ophthalmologists was ( 7. 25 ± 6. 58 ) , ( 5. 18 ± 5. 01 ) and ( 5. 18 ± 3. 47 ) seconds,and the total time of 1000 images of primary,intermediate and senior endocrinologists was 2. 02,1. 44 and 1. 44 hours;the mean and total time of AI system was (1. 62±0. 67) seconds and 0. 45 hours,and the reading time of AI system was significantly shorter than that of the doctor groups (all at P=0. 000). The diagnostic coincidence rates between previous and posterior part of primary endocrinologist, primary and intermediate ophthalmologist were significantly different (χ2=11. 986,6. 517,10. 896;all at P<0. 05),and the mean reading time in the posterior part was significantly shorter than that in the previous part of intermediate and senior endocrinologist and primary ophthalmologist (t=4. 175,8. 189,5. 160;all at P<0. 01). While the reading time of AI system remained stable throughout the process(χ2=3. 151,P=0. 103;t=0. 038,P=0. 970). Conclusions The ophthalmic AI system based on fundus images has a good diagnostic efficiency,and its diagnostic coincidence rate can compare with senior ophthalmologist,providing a new method and platform for large-scale DR screening.
ABSTRACT
Objective To investigate a diabetic retinopathy ( DR ) detection algorithm based on transfer learning in small sample dataset. Methods Total of 4465 fundus color photographs taken by Gaoyao People ' s Hospital was used as the full dataset. The model training strategies using fixed pre-trained parameters and fine-tuning pre-trained parameters were used as the transfer learning group to compare with the non-transfer learning strategy that randomly initializes parameters. These three training strategies were applied to the training of three deep learning networks:ResNet50,Inception V3 and NASNet. In addition,a small dataset randomly extracted from the full dataset was used to study the impact of the reduction of training data on different strategies. The accuracy and training time of the diagnostic model were used to analyze the performance of different training strategies. Results The best results in different network architectures were chosen. The accuracy of the model obtained by fine-tuning pre-training parameters strategy was 90. 9％,which was higher than the strategy of fixed pre-training parameters (88. 1％) and the strategy of randomly initializing parameters ( 88. 4％) . The training time for fixed pre-training parameters was 10 minutes,less than the strategy of fine-tuning pre-training parameters ( 16 hours ) and the strategy of randomly initializing parameters (24 hours). After the training data was reduced,the accuracy of the model obtained by the strategy of randomly initializing parameters decreased by 8. 6％ on average,while the accuracy of the transfer learning group decreased by 2. 5％ on average. Conclusions The proposed automated and novel DR detection algorithm based on fine-tune and NASNet structure maintains high accuracy in small sample dataset,is found to be robust,and effective for the preliminary diagnosis of DR.
ABSTRACT
Objective To evaluate the therapeutic effect and safety of ureteroscopy pneumatic lithotripsy(PL) and extracorporeal shock wave lithotripsy(ESWL) in the treatment of distal ureteral calculi.Methods 368 cases of distal ureteral calculi were divided into the PL treatment group(178 cases) and the ESWL treatment groups(190 cases).The clinical datas were compared between the two groups.Results PL treatment group 97.19% patients became stone free in 4 weeks,and in ESWL treatment group the stone free rate was 73.16%(P