Purpose: To develop and interpret optimal predictive models to identify epidermal growth factor receptor (EGFR) mutation status and subtypes in patients with lung adenocarcinoma based on multicentric ¹⁸F-FDG PET/CT data, and further construct a prognostic model to predict their clinical outcome.Methods: The ¹⁸F-FDG PET/CT imaging and clinical characters of 767 patients with lung adenocarcinoma from 4 cohorts were collected. Seventy-six radiomics candidates using cross-combination method to identity EGFR mutation status and subtypes were built. Further, Shapley additive explanations and local interpretable model-agnostic explanations were used for optimal models’ interpretation. Moreover, in order to predict the overall survival, a multivariate Cox proportional hazard model based on handcrafted radiomics features and clinical characteristics was constructed. The predictive performance and clinical net benefit of the models were evaluated via area under receiver operating characteristic (AUC), C-index and decision curve analysis. Results: Among the 76 radiomics candidates, light gradient boosting machine classifier (LGBM) combined with recursive feature elimination wrapped LGBM feature selection method achieved best performance in predicting EGFR mutation status (AUC reached 0.80, 0.61, 0.71 in the internal test cohort and two external test cohorts, respectively). And extreme gradient boosting classifier combined with support vector machine feature selection method achieved best performance in predicting EGFR subtypes (AUC reached 0.76, 0.63, 0.61 in the internal test cohort and two external test cohorts, respectively). The C-index of the Cox proportional hazard model achieved 0.863.Conclusions: The integration of cross-combination method and the external validation from multi-center data achieved a good prediction and generalization performance in predicting EGFR mutation status and its subtypes. The combination of handcrafted radiomics features and clinical factors achieved good performance in predicting prognosis. With the urgent needs of multicentric ¹⁸F-FDG PET/CT trails, robust and explainable radiomics models have great potential in decision making and prognosis prediction of lung adenocarcinoma.

To analyze the correlation of pulse characteristics at different acquisition positions, the pulse parameters that can identify different pulse and physiological information are given. In this paper, the relevance and difference of wrist pulse characteristics at Cun, Guan and Chi position were studied by pulse diagnosis instrument and time-frequency, frequency-domain and time-frequency domain analysis methods etc. Firstly, the pulse information of Cun, Guan and Chi position is collected from 206 healthy adults. The 66 pulse characteristics of each sampled location are extracted. In addition, the 66 feature indexes of each position are used to calculate the Spearman correlation coefficients among Cun, Guan, Chi position as well as the both hands in detail. Finally, the relevance and difference of wrist pulse at Cun, Guan and Chi position were studied and analyzed by significance test. The pulse characteristics with high correlation at Cun-Guan-Chi position of left and right hands are mainly power spectrum characteristics and time-frequency characteristics, while the features with high correlation between both hands mostly concentrate in the time domain characteristics. This work has studied the relationship between the position of pulse acquisition from quantitative and qualitative aspects, and laid a foundation for the objective diagnosis of pulse condition and the comprehensive disclosure of pulse information.

With the rapid development and application of artificial intelligence (AI) in medical field, the diagnostic ways of human health and the social medical structures have changed. Based on the concept of holism and the theory of syndrome differentiation and treatment, TCM realizes comprehensive informatization and intelligence with the help of AI technology in data mining, intelligent diagnosis and treatment, intelligent learning, and decision-making. Furthermore, the intelligent research of TCM technology will further promote the improvement in TCM diagnosis and treatment rules and the leaping development of TCM intelligent instruments. In this article, we performed a systematic review of scientific literature about TCM and AI. Moreover, the practical problems of TCM intellectualization, the current situation and demand of TCM, and the influence of AI in the TCM field are discussed by searching for literature using TCM scientific databases, reference lists, expert consultation, and targeted websites. Finally, we look forward to the application prospects of AI and propose a possible future direction of intelligent TCM in the current health-care system in China.

In this paper, a novel chaotic circuit with one memcapacitor is designed, which has abundant dynamic behaviors. As a potential application, combining the DNA binding mode and the characteristics of the chaotic system, a new image encryption scheme is proposed. Firstly, the original image is processed and generates a set of hash values by the SHA-3 algorithm. The generated hash value and original image are performed XOR operation. The hash values are processed to generate the initial value of the chaotic system by the Hamming distance. Furthermore, the elliptic curve with the hyperchaotic sequence is used to construct the Hill encryption matrix, and the plaintext image is permuted and encrypted. At last, the proposed chaotic system is used to scramble the image. The simulation results and theoretical analysis show that the chaotic system has abundant dynamic behaviors, and the proposed algorithm has a better anti-interference ability for image and information encryption.

The aim of the present study is to use entropy to explore the change of pulse generated by normal pregnant women with gestational. Firstly, the subjects were divided into early (E), middle (M), and late (L) three stages according to gestational age. Then, pulse signals of the Chi position of 90 pregnant women at different gestational ages were collected. Secondly, the four entropies, namely fuzzy entropy (FuEn), approximate entropy (ApEn), sample entropy (SamEn), and permutation entropy (PerEn), were applied to the analysis of the long-term pulse changes of the pregnancy. Finally, the related information about pulse in different stages of pregnancy is given by the analysis of four kinds of entropy. Furthermore, the statistical tests are conducted for further comparison, and the descriptive statistics and the results are presented. In addition, boxplots are employed to show the distribution of four entropies of pregnancy. This work has studied the changes in pulse during pregnancy from quantitative and qualitative aspects. Our results show that entropy improves the diagnostic value of pulse analysis during pregnancy and could be applied to facilitate noninvasive diagnosis of pregnant women’s physiological signals in the future.