Background It is hard to distinguish cerebral aneurysms from overlapping vessels in 2D digital subtraction angiography (DSA) images due to these images’ lack of spatial information. Objective The aims of this study were to (1) construct a deep learning diagnostic system to improve the ability to detect posterior communicating artery aneurysms on 2D DSA images and (2) validate the efficiency of the deep learning diagnostic system in 2D DSA aneurysm detection. Methods We proposed a 2-stage detection system. First, we established the region localization stage to automatically locate specific detection regions of raw 2D DSA sequences. Second, in the intracranial aneurysm detection stage, we constructed a bi-input+RetinaNet+convolutional long short-term memory (C-LSTM) framework to compare its performance for aneurysm detection with that of 3 existing frameworks. Each of the frameworks had a 5-fold cross-validation scheme. The receiver operating characteristic curve, the area under the curve (AUC) value, mean average precision, sensitivity, specificity, and accuracy were used to assess the abilities of different frameworks. Results A total of 255 patients with posterior communicating artery aneurysms and 20 patients without aneurysms were included in this study. The best AUC values of the RetinaNet, RetinaNet+C-LSTM, bi-input+RetinaNet, and bi-input+RetinaNet+C-LSTM frameworks were 0.95, 0.96, 0.92, and 0.97, respectively. The mean sensitivities of the RetinaNet, RetinaNet+C-LSTM, bi-input+RetinaNet, and bi-input+RetinaNet+C-LSTM frameworks and human experts were 89% (range 67.02%-98.43%), 88% (range 65.76%-98.06%), 87% (range 64.53%-97.66%), 89% (range 67.02%-98.43%), and 90% (range 68.30%-98.77%), respectively. The mean specificities of the RetinaNet, RetinaNet+C-LSTM, bi-input+RetinaNet, and bi-input+RetinaNet+C-LSTM frameworks and human experts were 80% (range 56.34%-94.27%), 89% (range 67.02%-98.43%), 86% (range 63.31%-97.24%), 93% (range 72.30%-99.56%), and 90% (range 68.30%-98.77%), respectively. The mean accuracies of the RetinaNet, RetinaNet+C-LSTM, bi-input+RetinaNet, and bi-input+RetinaNet+C-LSTM frameworks and human experts were 84.50% (range 69.57%-93.97%), 88.50% (range 74.44%-96.39%), 86.50% (range 71.97%-95.22%), 91% (range 77.63%-97.72%), and 90% (range 76.34%-97.21%), respectively. Conclusions According to our results, more spatial and temporal information can help improve the performance of the frameworks. Therefore, the bi-input+RetinaNet+C-LSTM framework had the best performance when compared to that of the other frameworks. Our study demonstrates that our system can assist physicians in detecting intracranial aneurysms on 2D DSA images.
The electron-withdrawing groups (EWGs) in the electrophilic alkenes employed in the Michael addition reaction are almost only CO 2 R, CN, COR, NO 2 , and SO 2 Ph. Although amides (CONR 1 R 2 ) are also typical electron-withdrawing groups and are of great importance in organic synthesis, they are scarcely employed as the EWGs of the electrophilic alkenes in the Michael addition reaction. In this work, the Michael reactions of acrylamide and its derivatives with cyclanones were successfully carried out in the presence of enough radical inhibitors. The amide groups play a key role in producing the preferred products. The N-substituted acrylamides, including N-monosubstituted and N,N-disubstituted acrylamides could react with cyclohexanone (CHn) to give the expected 2-carbamoylethyl derivatives; however, acrylamide reacting with cyclohexanone only produced ene-lactam. Cyclanones also have effects on the products, while the ring size of cyclanones influences the reaction yield and the α-substituent decides the ratio of resulting isomeric ene-lactams.
BACKGROUND It is hard to distinguish cerebral aneurysm from overlap vessels based on the 2D DSA images, for its lack the spatial information. OBJECTIVE The aim of this study is to construct a deep learning diagnostic system to improve the ability of detecting the PCoA aneurysm on 2D-DSA images and validate the efficiency of deep learning diagnostic system in 2D-DSA aneurysm detecting. METHODS We proposed a two stage detecting system. First, we established the regional localization stage (RLS) to automatically locate specific detection region of raw 2D-DSA sequences. And then, in the intracranial aneurysm detection stage (IADS) ,we build three different frames, RetinaNet, RetinaNet+LSTM, Bi-input+RetinaNet+LSTM, to detect the aneurysms. Each of the frame had fivefold cross-validation scheme. The area under curve (AUC), the receiver operating characteristic (ROC) curve, and mean average precision (mAP) were used to validate the efficiency of different frames. The sensitivity, specificity and accuracy were used to identify the ability of different frames. RESULTS 255 patients with PCoA aneurysms and 20 patients without aneurysm were included in this study. The best results of AUC of the RetinaNet, RetinaNet+LSTM, and Bi-input+RetinaNet+LSTM were 0.95, 0.96, and 0.97, respectively. The sensitivity of the RetinaNet, RetinaNet+LSTM, and Bi-input+RetinaNet+LSTM were 81.65% (59.40% to 94.76%), 87.91% (64.24% to 98.27%), 84.50% (69.57% to 93.97%), respectively. The specificity of the RetinaNet, RetinaNet+LSTM, and Bi-input+RetinaNet+LSTM were 88.89% (66.73% to 98.41%), 88.12% (66.06% to 98.08%), and 88.50% (74.44% to 96.39%), respectively. The accuracy of the RetinaNet, RetinaNet+LSTM, and Bi-input+RetinaNet+LSTM were 92.71% (71.29% to 99.54%), 89.42% (68.13% to 98.49%), and 91.00% (77.63% to 97.72%), respectively. CONCLUSIONS Two stage aneurysm detecting system can reduce time cost and the computation load. According to our results, more spatial and temporal information can help improve the performance of the frames, so that Bi-input+RetinaNet+LSTM has the best performance compared to other frames. And our study can demonstrate that our system was feasible to assist doctor to detect intracranial aneurysm on 2D-DSA images.
BACKGROUND It is hard to distinguish cerebral aneurysms from overlapping vessels based on 2D DSA images due to their lack of spatial information. OBJECTIVE The aim of this study was to construct a deep learning diagnostic system to improve the ability to detect posterior communicating artery (PCoA) aneurysms on 2D-DSA images and validate the efficiency of the deep learning diagnostic system in 2D-DSA aneurysm detection. METHODS We proposed a two-stage detecting system. First, we established the regional localization stage (RLS) to automatically locate specific detection regions of raw 2D-DSA sequences. Then, in the intracranial aneurysm detection stage (IADS), we constructed the Bi-input+RetinaNet+C-LSTM framework to compare the performance of aneurysm detection with the existing three frameworks. Each of the frameworks had a fivefold cross-validation scheme. The area under the curve (AUC), the receiver operating characteristic (ROC) curve, and mean average precision (mAP) were used to validate the efficiency of different frameworks. The sensitivity, specificity and accuracy were used to identify the abilities of different frameworks. RESULTS A total of 255 patients with PCoA aneurysms and 20 patients without aneurysms were included in this study. The best AUC results of RetinaNet, RetinaNet+C-LSTM, Bi-input+RetinaNet and Bi-input+RetinaNet+C-LSTM were 0.95, 0.96, 0.92 and 0.97, respectively. The sensitivities of RetinaNet, RetinaNet+C-LSTM, Bi-input+RetinaNet, Bi-input+RetinaNet+C-LSTM, and human experts were 89.00% (67.02% to 98.43%), 88.00% (65.76% to 98.06%), 87.00% (64.53% to 97.66%), 89.00% (67.02% to 98.43%), and 90% (68.30% to 98.77%), respectively. The specificity of RetinaNet, RetinaNet+C-LSTM, Bi-input+RetinaNet, Bi-input+RetinaNet+C-LSTM, and human expert were 80.00% (56.34% to 94.27%), 89.00% (67.02% to 98.43%), 86.00% (63.31% to 97.24%), 93.00% (72.30% to 99.56%), and 90% (68.30% to 98.77%), respectively. The accuracies of RetinaNet, RetinaNet+C-LSTM, Bi-input+RetinaNet, Bi-input+RetinaNet+C-LSTM, and human experts were 84.50% (69.57% to 93.97%), 88.50% (74.44% to 96.39%), 86.50% (71.97% to 95.22%), 91.00% (77.63% to 97.72%), and 90.00% (76.34% to 97.21%), respectively. CONCLUSIONS A two-stage aneurysm detection system can reduce the time cost and the computational load. According to our results, more spatial and temporal information can help improve the performances of the frameworks so that Bi-input+RetinaNet+C-LSTM has the best performance compared to the other frameworks. Our study demonstrates that our system can assist doctors in detecting intracranial aneurysms on 2D-DSA images.
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