Driverless artificial intelligence framework for the identification of malignant pleural effusion

Li, Yuan; Tian, Shan; Huang, Yajun; Dong, Weiguo

doi:10.1016/j.tranon.2020.100896

Cited by 31 publications

(19 citation statements)

References 45 publications

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“…In recent years, the development of information science and technology has been very rapid, and the development of science and technology has led to the progress of the Internet and multimedia technology [1,2]. In the face of scientific and technological progress, university teaching management must also keep up with the pace of the times and can no longer manage in accordance with the traditional methods and use modern technology to improve traditional management methods and the level of teaching management [3,4].…”

Section: Introductionmentioning

confidence: 99%

Student Behavior Analysis and Research Model Based on Clustering Technology

Alfred

Wang³

2021

Mobile Information Systems

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Now, entering the 21st century, with the continuous improvement of my country’s higher education level, the enrollment rate of all colleges and universities across the country is increasing year by year. Faced with the information management of a large number of students, the workload and work pressure of consultants at various universities have doubled. The rapid and effective development of modern computer software and hardware has also initiated and effectively developed the informatization process of universities. The student management system is the core and foundation of the entire school education management system. This study mainly introduces the application of student behavior analysis and research models based on clustering technology. This paper uses the application research of student behavior analysis and research model based on clustering technology, uses clustering technology to analyze student behavior, and reasonably analyzes the feasibility of KMEANS algorithm and campus data mining. The cluster analysis algorithm is used to divide students into different groups according to the characteristics of the students, and then, data analysis and data association rules’ mining are performed on each group of students. At the same time, the decision tree algorithm is used to predict the future of students based on the historical data of the students and the current data of the students. The development status of the school helps the school to understand the situation of the students in real time, make predictions and warnings for possible situations, provide personalized applications for teachers and students, and provide decision-making support for the management. It can be seen from the experimental analysis that the application of student behavior analysis and research models based on clustering technology has increased the efficiency of student education by 17%. The limitations of student behavior analysis and research on clustering technology provide good applications for the KMEANS algorithm. Analysis, discussion, and summary of the methods and approaches are obtained to enrich the academic research results.

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Section: Introductionmentioning

confidence: 99%

Student Behavior Analysis and Research Model Based on Clustering Technology

Alfred

Wang³

2021

Mobile Information Systems

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“…The proposed nomogram in our study incorporated the seven most significant variables (ESR, effusion CEA, effusion CA19-9, effusion CYFRA21-1, CEA ratio, effusion ADA, and CR) and provided favorable calibration and discrimination in both derivation and validation sets. Some studies have applied an artificial intelligence framework for the diagnosis of MPE and BPE; such models require specific software that would promote their wide application (33,34). Herein, we modified the nomogram into a scoring system for clinical application.…”

Section: Discussionmentioning

confidence: 99%

“…Some studies have applied an artificial intelligence framework for the diagnosis of MPE and BPE; such models require specific software that would promote their wide application ( 33 , 34 ). Herein, we modified the nomogram into a scoring system for clinical application.…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of a Scoring System for Early Diagnosis of Malignant Pleural Effusion Based on a Nomogram

Liang

Yuan

et al. 2021

Front. Oncol.

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BackgroundThe diagnostic value of clinical and laboratory features to differentiate between malignant pleural effusion (MPE) and benign pleural effusion (BPE) has not yet been established.ObjectivesThe present study aimed to develop and validate the diagnostic accuracy of a scoring system based on a nomogram to distinguish MPE from BPE.MethodsA total of 1,239 eligible patients with PE were recruited in this study and randomly divided into a training set and an internal validation set at a ratio of 7:3. Logistic regression analysis was performed in the training set, and a nomogram was developed using selected predictors. The diagnostic accuracy of an innovative scoring system based on the nomogram was established and validated in the training, internal validation, and external validation sets (n = 217). The discriminatory power and the calibration and clinical values of the prediction model were evaluated.ResultsSeven variables [effusion carcinoembryonic antigen (CEA), effusion adenosine deaminase (ADA), erythrocyte sedimentation rate (ESR), PE/serum CEA ratio (CEA ratio), effusion carbohydrate antigen 19-9 (CA19-9), effusion cytokeratin 19 fragment (CYFRA 21-1), and serum lactate dehydrogenase (LDH)/effusion ADA ratio (cancer ratio, CR)] were validated and used to develop a nomogram. The prediction model showed both good discrimination and calibration capabilities for all sets. A scoring system was established based on the nomogram scores to distinguish MPE from BPE. The scoring system showed favorable diagnostic performance in the training set [area under the curve (AUC) = 0.955, 95% confidence interval (CI) = 0.942–0.968], the internal validation set (AUC = 0.952, 95% CI = 0.932–0.973), and the external validation set (AUC = 0.973, 95% CI = 0.956–0.990). In addition, the scoring system achieved satisfactory discriminative abilities at separating lung cancer-associated MPE from tuberculous pleurisy effusion (TPE) in the combined training and validation sets.ConclusionsThe present study developed and validated a scoring system based on seven parameters. The scoring system exhibited a reliable diagnostic performance in distinguishing MPE from BPE and might guide clinical decision-making.

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“…Deep learning belongs to the class of machine learning that can successively identify more abstract information from the input data [11] , [12] , [13] . Deep learning has progressed remarkably in the field of oncology, and has been demonstrated to be superior to conventional machine learning techniques [ 14 , 15 ]. Convolutional neural network (CNN) is a high-efficient deep learning method for image recognition and has excelled in quite a few images interpretation tasks [ 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

Accurate diagnosis and prognosis prediction of gastric cancer using deep learning on digital pathological images: A retrospective multicentre study

et al. 2021

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Background: To reduce the high incidence and mortality of gastric cancer (GC), we aimed to develop deep learning-based models to assist in predicting the diagnosis and overall survival (OS) of GC patients using pathological images. Methods: 2333 hematoxylin and eosin-stained pathological pictures of 1037 GC patients were collected from two cohorts to develop our algorithms, Renmin Hospital of Wuhan University (RHWU) and the Cancer Genome Atlas (TCGA). Additionally, we gained 175 digital pictures of 91 GC patients from National Human Genetic Resources Sharing Service Platform (NHGRP), served as the independent external validation set. Two models were developed using artificial intelligence (AI), one named GastroMIL for diagnosing GC, and the other named MIL-GC for predicting outcome of GC. Findings: The discriminatory power of GastroMIL achieved accuracy 0.920 in the external validation set, superior to that of the junior pathologist and comparable to that of expert pathologists. In the prognostic model, Cindices for survival prediction of internal and external validation sets were 0.671 and 0.657, respectively. Moreover, the risk score output by MIL-GC in the external validation set was proved to be a strong predictor of OS both in the univariate (HR = 2.414, P < 0.0001) and multivariable (HR = 1.803, P = 0.043) analyses. The predicting process is available at an online website (https://baigao.github.io/Pathologic-Prognostic-Analysis/). Interpretation: Our study developed AI models and contributed to predicting precise diagnosis and prognosis of GC patients, which will offer assistance to choose appropriate treatment to improve the survival status of GC patients. Funding: Not applicable.

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Driverless artificial intelligence framework for the identification of malignant pleural effusion

Cited by 31 publications

References 45 publications

Student Behavior Analysis and Research Model Based on Clustering Technology

Student Behavior Analysis and Research Model Based on Clustering Technology

Development and Validation of a Scoring System for Early Diagnosis of Malignant Pleural Effusion Based on a Nomogram

Accurate diagnosis and prognosis prediction of gastric cancer using deep learning on digital pathological images: A retrospective multicentre study

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