scholarly journals A Novel Intelligent Scan Assistant System for Early Pregnancy Diagnosis by Ultrasound: Clinical Decision Support System Evaluation Study (Preprint)

2019 ◽  
Author(s):  
Ferdinand Dhombres ◽  
Paul Maurice ◽  
Lucie Guilbaud ◽  
Loriane Franchinard ◽  
Barbara Dias ◽  
...  
Keyword(s):  
Decision Support ◽  
Image Quality ◽  
Decision Support System ◽  
Knowledge Base ◽  
Support System ◽  
Early Pregnancy ◽  
Diagnostic Performance ◽  
Transvaginal Ultrasound ◽  
Precise Diagnosis ◽  
Exact Diagnosis

BACKGROUND Early pregnancy ultrasound scans are usually performed by nonexpert examiners in obstetrics/gynecology (OB/GYN) emergency departments. Establishing the precise diagnosis of pregnancy location is key for appropriate management of early pregnancies, and experts are usually able to locate a pregnancy in the first scan. A decision-support system based on a semantic, expert-validated knowledge base may improve the diagnostic performance of nonexpert examiners for early pregnancy transvaginal ultrasound. OBJECTIVE This study aims to evaluate a novel Intelligent Scan Assistant System for early pregnancy ultrasound to diagnose the pregnancy location and determine the image quality. METHODS Two trainees performed virtual transvaginal ultrasound examinations of early pregnancy cases with and without the system. The ultrasound images and reports were blindly reviewed by two experts using scoring methods. A diagnosis of pregnancy location and ultrasound image quality were compared between scans performed with and without the system. RESULTS Each trainee performed a virtual vaginal examination for all 32 cases with and without use of the system. The analysis of the 128 resulting scans showed higher quality of the images (quality score: +23%; P<.001), less images per scan (4.6 vs 6.3 [without the CDSS]; P<.001), and higher confidence in reporting conclusions (trust score: +20%; P<.001) with use of the system. Further, use of the system cost an additional 8 minutes per scan. We observed a correct diagnosis of pregnancy location in 39 (61%) and 52 (81%) of 64 scans in the nonassisted mode and assisted mode, respectively. Additionally, an exact diagnosis (with precise ectopic location) was made in 30 (47%) and 49 (73%) of the 64 scans without and with use of the system, respectively. These differences in diagnostic performance (+20% for correct location diagnosis and +30% for exact diagnosis) were both statistically significant (P=.002 and P<.001, respectively). CONCLUSIONS The Intelligent Scan Assistant System is based on an expert-validated knowledge base and demonstrates significant improvement in early pregnancy scanning, both in diagnostic performance (pregnancy location and precise diagnosis) and scan quality (selection of images, confidence, and image quality).

scholarly journals Intelligent decision support method in a typical situation

2021 ◽  
Vol 11 (1) ◽  
pp. 126-136
Author(s):  
V.V. Antonov ◽  
◽  
K.A. Konev
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Knowledge Base ◽  
Business Process ◽  
Support System ◽  
System Integration ◽  
General Structure ◽  
Typical Situation ◽  
Ontological Model ◽  
Decision Support Method

The article discusses a decision support method using a knowledge base. The relevance of the study of issues related to decision-making in typical situations is shown. In order to increase the effectiveness of management activities, the is-sues of system integration of the regulatory framework, ontological model and knowledge base of the intelligent sub-system of decision support within the framework of the business process are considered. In support of the proposed method, a model has been formed for replenishing the knowledge base both at the structural and analytical levels, which demonstrates the connection between the most important elements of the system: the ontological model, the knowledge base and the normative subsystem. An example of using the proposed scheme is shown. To demonstrate the model of functioning of the decision support system, an algorithm for replenishing the knowledge base is proposed and described. As a conceptual basis for the formal description of the model, operations for working with knowledge are described in the set-theoretic aspect. The principles of adaptation of the ontological model as an information object for linking with the knowledge base are considered. The conceptual diagram of the general structure of the ontological model for making decisions within the framework of the business process as a set of interrelated concepts is proposed and demonstrated. An information model of a specialized database has been developed and presented, serving as a technical basis for building a knowledge base of a decision support system in a typical situation, its main structural el-ements, the principles of their interrelation and an approach to ensuring the consistency of its internal structure are de-scribed.

Mobile Decision Support System with Dynamic Knowledge Base using Decision Tree and Case-Based Reasoning

2018 ◽  
Vol 29 (1) ◽  
pp. 1
Author(s):  
Muhammad Dzulhimi Azmi ◽  
Muhammad Rafie Hj Mohd Arshad ◽  
Hasimah Hj Mohamed
Keyword(s):  
Decision Support ◽  
Decision Tree ◽  
Decision Support System ◽  
Knowledge Base ◽  
Support System ◽  
Case Based Reasoning ◽  
Case Based

scholarly journals The Research of Clinical Decision Support System Based on Three-Layer Knowledge Base Model

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Yicheng Jiang ◽  
Bensheng Qiu ◽  
Chunsheng Xu ◽  
Chuanfu Li
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Knowledge Base ◽  
Clinical Decision Support ◽  
Support System ◽  
Naive Bayes ◽  
Clinical Decision ◽  
Disease Symptom ◽  
Layer Model ◽  
Bayes Algorithm

In many clinical decision support systems, a two-layer knowledge base model (disease-symptom) of rule reasoning is used. This model often does not express knowledge very well since it simply infers disease from the presence of certain symptoms. In this study, we propose a three-layer knowledge base model (disease-symptom-property) to utilize more useful information in inference. The system iteratively calculates the probability of patients who may suffer from diseases based on a multisymptom naive Bayes algorithm, in which the specificity of these disease symptoms is weighted by the estimation of the degree of contribution to diagnose the disease. It significantly reduces the dependencies between attributes to apply the naive Bayes algorithm more properly. Then, the online learning process for parameter optimization of the inference engine was completed. At last, our decision support system utilizing the three-layer model was formally evaluated by two experienced doctors. By comparisons between prediction results and clinical results, our system can provide effective clinical recommendations to doctors. Moreover, we found that the three-layer model can improve the accuracy of predictions compared with the two-layer model. In light of some of the limitations of this study, we also identify and discuss several areas that need continued improvement.

scholarly journals Effects of a Decision Support System on Physicians' Diagnostic Performance

1999 ◽  
Vol 6 (5) ◽  
pp. 420-427 ◽  
Author(s):  
E. S. Berner ◽  
R. S. Maisiak ◽  
C. G. Cobbs ◽  
O. D. Taunton
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Diagnostic Performance

scholarly journals A Novel Knowledge Base Decision Support System Model for Breast Cancer Treatment

2010 ◽  
Vol 1 (2) ◽  
pp. 97 ◽  
Author(s):  
Rajesh Kumar Sinha ◽  
Manohara MM Pai ◽  
M S Vidyasagar ◽  
Vadhiraja B M
Keyword(s):  
Breast Cancer ◽  
Decision Support ◽  
Decision Support System ◽  
Cancer Treatment ◽  
Knowledge Base ◽  
Support System ◽  
Breast Cancer Treatment ◽  
System Model
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