Application value of computer-aided diagnosis in diagnosing pneumoconiosis / 中国职业医学

OBJECTIVE: To explore the application value of computer-aided diagnosis technology based on deep residual network in the diagnosis of occupational pneumoconiosis(hereinafter referred to as pneumoconiosis). METHODS: A total of 5 424 digital radiography chest images were collected from occupational health examiners using a convenient sampling method.These images were used to establish a data set. After training with the data set, the pneumoconiosis computer-aided diagnosis system was used to independently diagnose the test set images(50 positive and negative cases each) and output a positive probability value. Six diagnostic physicians with varied ages and different experiences performed independent diagnosis on the test set and assisted diagnosis with reference to computer results. The diagnostic accuracy was evaluated using the area under the receiver operating characteristic curve(AUC) value, sensitivity, and specificity.The Kappa consistency test was used to evaluate the diagnostic consistency. RESULTS: The AUC value, sensitivity, specificity, and Kappa value of pneumoconiosis diagnosis increased after using computer-aided diagnosis. The sensitivity increased from 0.74 to 0.85(P<0.05)and the Kappa value increased from 0.64 to 0.79(P<0.05). The AUC value increased from 0.90 to 0.95, and the specificity increased from 0.89 to 0.94, but there were no statistical difference(P<0.05). CONCLUSION: Computer-aided diagnosis can improve the sensitivity and consistency of pneumoconiosis screening and reduce the differences in diagnosis among physicians.

Modeling of Agent Cognition in Extensive Games via Artificial Neural Networks.

The decision-making process, which is regarded as cognitive and ubiquitous, has been exploited in diverse fields, such as psychology, economics, and artificial intelligence. This paper considers the problem of modeling agent cognition in a class of game-theoretic decision-making scenarios called extensive games. We present a novel framework in which artificial neural networks are incorporated to simulate agent cognition regarding the structure of the underlying game and the goodness of the game situations therein. An algorithmic procedure is investigated to describe the process for solving games with cognition, and then, a new equilibrium concept is proposed as a refinement of the classical one-subgame perfect equilibrium-by involving players' cognitive reasoning. Moreover, a series of results concerning the computational complexity, soundness, and completeness of the algorithm, as well as the existence of an equilibrium solution, is obtained. This framework, which is shown to be general enough to model the way in which AlphaGo plays Go, may offer a means for bridging the gap between theoretical models and practical problem-solving.

Synthesis and inhibition of NO production activities of 1,6-O, O-diacylbritannilactone derivatives / 中国药科大学学报

Eight 1,6-O,O-diacetylbritannilactone (OABL) derivatives (compounds 1-8) were synthesized by esterification or reduction of 1-O-diacetylbritannilactone (ABL) isolated from Inula japonica.All derivatives were evaluated for their anti-inflammation activities through the determination of inhibition of nitric oxide (NO) production in lipopolysaccharide (LPS)-induced macrophages.As results,compounds 5-8 (IC50 ＜ 2 μmol/L) exhibited more potent inhibition of NO production activities than the lead compound OABL.