Assessment of risk based on variant pathways and establishment of an artificial neural network model of thyroid cancer

Prediction Model for Delayed Behavior of Early Ambulation After Surgery for Varicose Veins of the Lower Extremity: A Prospective Case-Control Study

OBJECTIVE

To analyze influencing factors and establish a prediction model for delayed behavior of early ambulation after surgery for varicose veins of the lower extremity (VVLE).

DESIGN

A prospective case-control study.

SETTING

Patients with VVLE were recruited from 2 local hospitals.

PARTICIPANTS

In total, 498 patients with VVLE were selected using convenience sampling and divided into a training set and a test set.

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURES

We collected information from the selected participants before surgery and followed up until the day after surgery, then divided them into a normal and delayed ambulation group. Propensity score matching was applied to all participants by type of surgery and anesthesia. All the characteristics in the 2 groups were compared using logistic regression, back propagation neural network (BPNN), and decision tree models. The accuracy, sensitivity, specificity, and area under the curve (AUC) values of the 3 models were compared to determine the optimal model.

RESULTS

A total of 406 participants were included after propensity score matching. The AUC values for the training sets of logistic regression, BPNN, and decision tree models were 0.850, 0.932, and 0.757, respectively. The AUC values for the test sets were 0.928, 0.984, and 0.776, respectively. A BPNN was the optimal model. Social Support Rating Scale score, preoperative 30-second sit-stand test score, Clinical-Etiology-Anatomy-Pathophysiology (CEAP) grade, Medical Coping Modes Questionnaire score, and whether you know the need for early ambulation, in descending order of the result of a BPNN model. A probability value greater than 0.56 indicated delayed behavior of early ambulation.

CONCLUSIONS

Clinicians should pay more attention to those with lower Social Support Rating Scale scores, poor lower limb strength, a higher CEAP grade, and poor medical coping ability, and make patients aware of the necessity and importance of early ambulation, thereby assisting decision-making regarding postoperative rehabilitation. Further research is needed to improve the method, add more variables, and transform the model into a scale to screen and intervene in the delayed behavior of early ambulation of VVLE in advance.

Applications of machine and deep learning to thyroid cytology and histopathology: a review

This review synthesises past research into how machine and deep learning can improve the cyto- and histopathology processing pipelines for thyroid cancer diagnosis. The current gold-standard preoperative technique of fine-needle aspiration cytology has high interobserver variability, often returns indeterminate samples and cannot reliably identify some pathologies; histopathology analysis addresses these issues to an extent, but it requires surgical resection of the suspicious lesions so cannot influence preoperative decisions. Motivated by these issues, as well as by the chronic shortage of trained pathologists, much research has been conducted into how artificial intelligence could improve current pipelines and reduce the pressure on clinicians. Many past studies have indicated the significant potential of automated image analysis in classifying thyroid lesions, particularly for those of papillary thyroid carcinoma, but these have generally been retrospective, so questions remain about both the practical efficacy of these automated tools and the realities of integrating them into clinical workflows. Furthermore, the nature of thyroid lesion classification is significantly more nuanced in practice than many current studies have addressed, and this, along with the heterogeneous nature of processing pipelines in different laboratories, means that no solution has proven itself robust enough for clinical adoption. There are, therefore, multiple avenues for future research: examine the practical implementation of these algorithms as pathologist decision-support systems; improve interpretability, which is necessary for developing trust with clinicians and regulators; and investigate multiclassification on diverse multicentre datasets, aiming for methods that demonstrate high performance in a process- and equipment-agnostic manner.

Prediction model for the risk of osteoporosis incorporating factors of disease history and living habits in physical examination of population in Chongqing, Southwest China: based on artificial neural network

Background

Osteoporosis is a gradually recognized health problem with risks related to disease history and living habits. This study aims to establish the optimal prediction model by comparing the performance of four prediction models that incorporated disease history and living habits in predicting the risk of Osteoporosis in Chongqing adults.

Methods

We conduct a cross-sectional survey with convenience sampling in this study. We use a questionnaire From January 2019 to December 2019 to collect data on disease history and adults’ living habits who got dual-energy X-ray absorptiometry. We established the prediction models of osteoporosis in three steps. Firstly, we performed feature selection to identify risk factors related to osteoporosis. Secondly, the qualified participants were randomly divided into a training set and a test set in the ratio of 7:3. Then the prediction models of osteoporosis were established based on Artificial Neural Network (ANN), Deep Belief Network (DBN), Support Vector Machine (SVM) and combinatorial heuristic method (Genetic Algorithm - Decision Tree (GA-DT)). Finally, we compared the prediction models’ performance through accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC) to select the optimal prediction model.

Results

The univariate logistic model found that taking calcium tablet (odds ratio [OR] = 0.431), SBP (OR = 1.010), fracture (OR = 1.796), coronary heart disease (OR = 4.299), drinking alcohol (OR = 1.835), physical exercise (OR = 0.747) and other factors were related to the risk of osteoporosis. The AUCs of the training set and test set of the prediction models based on ANN, DBN, SVM and GA-DT were 0.901, 0.762; 0.622, 0.618; 0.698, 0.627; 0.744, 0.724, respectively. After evaluating four prediction models’ performance, we selected a three-layer back propagation neural network (BPNN) with 18, 4, and 1 neuron in the input layer, hidden and output layers respectively, as the optimal prediction model. When the probability was greater than 0.330, osteoporosis would occur.

Conclusions

Compared with DBN, SVM and GA-DT, the established ANN model had the best prediction ability and can be used to predict the risk of osteoporosis in physical examination of the Chongqing population. The model needs to be further improved through large sample research.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-11002-5.

Artificial Intelligence for Personalized Medicine in Thyroid Cancer: Current Status and Future Perspectives

Thyroid cancers (TC) have increasingly been detected following advances in diagnostic methods. Risk stratification guided by refined information becomes a crucial step toward the goal of personalized medicine. The diagnosis of TC mainly relies on imaging analysis, but visual examination may not reveal much information and not enable comprehensive analysis. Artificial intelligence (AI) is a technology used to extract and quantify key image information by simulating complex human functions. This latent, precise information contributes to stratify TC on the distinct risk and drives tailored management to transit from the surface (population-based) to a point (individual-based). In this review, we started with several challenges regarding personalized care in TC, for example, inconsistent rating ability of ultrasound physicians, uncertainty in cytopathological diagnosis, difficulty in discriminating follicular neoplasms, and inaccurate prognostication. We then analyzed and summarized the advances of AI to extract and analyze morphological, textural, and molecular features to reveal the ground truth of TC. Consequently, their combination with AI technology will make individual medical strategies possible.

An artificial neural network model predicting pathologic nodal metastases in clinical stage I-II esophageal squamous cell carcinoma patients

Background

Current preoperative staging for lymph nodal status remains inaccurate. The purpose of this study was to build an artificial neural network (ANN) model to predict pathologic nodal involvement in clinical stage I–II esophageal squamous cell carcinoma (ESCC) patients and then validated the performance of the model.

Methods

A total of 523 patients (training set: 350; test set: 173) with clinical staging I–II ESCC who underwent esophagectomy and reconstruction were enrolled in this study. Their post-surgical pathological results were assessed and analysed. An ANN model was established for predicting pathologic nodal positive patients in the training set, which was validated in the test set. A receiver operating characteristic (ROC) curve was also created to illustrate the performance of the predictive model.

Results

Of the enrolled 523 patients with ESCC, 41.3% of the patients were confirmed pathologic nodal positive (216/523). The ANN staging system identified the tumour invasion depth, tumour length, dysphagia, tumour differentiation and lymphovascular invasion (LVI) as predictors for pathologic lymph node metastases. The C-index for the ANN model verified in the test set was 0.852, which demonstrated that the ANN model had a good predictive performance.

Conclusions

The ANN model presented good performance for predicting pathologic lymph node metastasis and added indicators not included in current staging criteria and might help improve the staging strategies.