Diagnostic Support System Research Articles

1P2-I09 超音波診断における生体信号所為に基づいた検査者疲労軽減に関する一考察

Examination of echography greatly depends on an operator's expertise and experience. So, Diagnosis and treatment support system is necessary to reduce the doctor's load. Although we had so far developed the diagnosis and treatment support system for echography, the method of evaluating this system quantitatively was not established. On the other hand, although there is much research about measurement of tiredness or stress, there is no research applied to echography. In this paper, we have constructed the diagnostic support system for echography based on a biological signal. First, we have measured electromyograms of wrists and waists of the experimenters at ultrasound probe scanning. Next, we also measured the effect of posture of the experimenter and how to grasp the probe to the body surface. We have analyzed strain of physical base on time-variable average frequency (MNF) of EMG on each condition, and discussed those relations from each time change and correlation chart of MNF. As a result, we have found that it is possible to reduce a physical fatigue of echography by considering the tips of the posture and the probe scan.

Intelligent electrocardiogram pattern classification and recognition using low‐cost cardio‐care system

Electrocardiogram (ECG) contains detailed information regarding incidental abnormality of a subject. Manual analysis of a long time ECG record is a lengthy process. Computerised ECG analysis supports clinicians in decision making. While designing a low-cost diagnostic support system, constraints on the system resources limit the processing speed, eventually affecting the reliability. To resolve these issues, three key factors have been addressed in this study: the feature extraction method, total number of features and the database used. For feature extraction, ‘polar Teager energy’ algorithm has been developed, yielding nearly 70% saving in processing time as compared to other well-known methods. Using features with linear relationship leads to reduction in feature vector dimension, without compromising its classification performance. Therefore the linear relationship between two ECG features, namely ‘informational entropy’(S) and ‘mean Teager energy’ has been revealed. These features are utilised for ECG beat classification using ‘fuzzy C-means clustering’ algorithm. The algorithm is evaluated using the MIT-BIH database and then tested by ECG measured with the cardio-care unit. The QRS detection performance of the proposed method is very good, with 0.27% detection error rate. For classification of ECG beats, average sensitivity and positive prediction rate achieved are 98.93% each.

Abstract Computation in Schizophrenia Detection through Artificial Neural Network Based Systems

Schizophrenia stands for a long-lasting state of mental uncertainty that may bring to an end the relation among behavior, thought, and emotion; that is, it may lead to unreliable perception, not suitable actions and feelings, and a sense of mental fragmentation. Indeed, its diagnosis is done over a large period of time; continuos signs of the disturbance persist for at least 6 (six) months. Once detected, the psychiatrist diagnosis is made through the clinical interview and a series of psychic tests, addressed mainly to avoid the diagnosis of other mental states or diseases. Undeniably, the main problem with identifying schizophrenia is the difficulty to distinguish its symptoms from those associated to different untidiness or roles. Therefore, this work will focus on the development of a diagnostic support system, in terms of its knowledge representation and reasoning procedures, based on a blended of Logic Programming and Artificial Neural Networks approaches to computing, taking advantage of a novel approach to knowledge representation and reasoning, which aims to solve the problems associated in the handling (i.e., to stand for and reason) of defective information.

1P1-E07 超音波診断・治療補助ロボットReDATによる協調動作に基づいた断層像取得アプローチ実験

1P1-E07 超音波診断・治療補助ロボットReDATによる協調動作に基づいた断層像取得アプローチ実験

The study of the image processing for the diagnostic support system of gastrointestinal endoscope image

The study of the image processing for the diagnostic support system of gastrointestinal endoscope image

Early diagnostic suggestions improve accuracy of GPs: a randomised controlled trial using computer-simulated patients.

BackgroundDesigners of computerised diagnostic support systems (CDSSs) expect physicians to notice when they need advice and enter into the CDSS all information that they have gathered about the patient. The poor use of CDSSs and the tendency not to follow advice once a leading diagnosis emerges would question this expectation.AimTo determine whether providing GPs with diagnoses to consider before they start testing hypotheses improves accuracy.Design and settingMixed factorial design, where 297 GPs diagnosed nine patient cases, differing in difficulty, in one of three experimental conditions: control, early support, or late support.MethodData were collected over the internet. After reading some initial information about the patient and the reason for encounter, GPs requested further information for diagnosis and management. Those receiving early support were shown a list of possible diagnoses before gathering further information. In late support, GPs first gave a diagnosis and were then shown which other diagnoses they could still not discount.ResultsEarly support significantly improved diagnostic accuracy over control (odds ratio [OR] 1.31; 95% confidence interval [95%CI] = 1.03 to 1.66, P = 0.027), while late support did not (OR 1.10; 95% CI = 0.88 to 1.37). An absolute improvement of 6% with early support was obtained. There was no significant interaction with case difficulty and no effect of GP experience on accuracy. No differences in information search were detected between experimental conditions.ConclusionReminding GPs of diagnoses to consider before they start testing hypotheses can improve diagnostic accuracy irrespective of case difficulty, without lengthening information search.

A Novel Architecture and Analysis of Challenges for Combining Text and Image for Medical Image Retrieval

The introduction of digital information storage and internet technologies has changed diagnostic radiology practices significantly. Radiological support systems including PACS (Picture Archiving and Communication System), SRS (Structured Reporting System) and image and text retrieval systems have been developed to assist radiologists. In this article we analyse the integration of image and text retrieval is promising for enhancing radiological teaching, research and diagnosis. Presentation of similar images as diagnostic aids has proven to be helpful in domains of radiology such as chest radiographs, lung CT and mammograms. We propose a diagnostic support system based on a combination of image and text retrieval. Our approach has the potential to increase the radiologist’s confidence in his/her diagnosis which could translate to greater accuracy and reduced health care costs.

鋼構造物の塗膜劣化画像診断支援システムの構築

鋼構造物の塗膜劣化画像診断支援システムの構築

Automated flow quantification for spin labeling MR imaging

The Time-Spatial Labeling Inversion Pulse (Time-SLIP) technique enables tracing of regional fluid flows without the use of contrast medium. The objective of this study is to quantify automatically slow and complex fluid flows using the Time-SLIP technique. Series images were acquired with a 1.5-T MRI scanner using the Time-SLIP technique with half-Fourier fast spin-echo (FSE) and balanced steady-state free precession (bSSFP) sequences. In this method, labeled fluid regions in images were automatically detected based on image processing techniques for a given point. The flow velocity of the labeled fluid region was calculated using regression fitting for the region's position. To evaluate our method, constant and non-constant laminar flows in a water phantom were studied. In addition, volunteer experiments were conducted to quantify the flow of cerebrospinal fluid. In the constant flow experiments the correlation factor r (2) between the flow velocity calculated from our method and the laminar peak velocity calculated from the volumetric flow rate was 0.9992 for the FSE sequence and 0.9982 for the bSSFP sequence. In the non-constant flow study, the flow velocity was calculated accurately for any period inversion time even when the flow velocity was changed, and the quantification error was negligible. In the volunteer experiments, r (2) between the flow velocity calculated by the proposed method and that obtained by manual annotation was 0.9383. The experimental results showed that our proposed method can quickly and accurately provide information on flow velocities especially for slower and complex flows. Our method is, therefore, expected to be useful in diagnostic support systems.

A Note on Hypertension Classification Scheme and Soft Computing Decision Making System

Nowadays young professionals are a soft target of hypertension due to the increased work pressure and poor tolerance. Many people have high blood pressure for years without knowing it. Most of the time, there are no symptoms, but when this condition goes untreated it damages arteries and vital organs throughout the body and that is why it is also termed as the silent killer. Complications arising from hypertension could lead to stroke and heart failure. Soft computing approach provides a sharper conclusion from vague, ambiguous, and imprecise data (generally found in medical field) using linguistic variables. In this study, a soft computing diagnostic support system for the risk assessment of hypertension is proposed.

Percutaneous transhepatic biliary drainage assisted by real-time virtual sonography: a retrospective study

BackgroundReal-time virtual sonography (RVS) is a diagnostic imaging support system that can synchronize with ultrasound images in conjunction with computed tomography or magnetic resonance images using magnetic navigation system. RVS has been applied in clinical practice to perform such procedures as radiofrequency ablation and biopsy; however, the application of RVS for percutaneous transhepatic biliary drainage (PTBD) is rare.MethodsBetween 2007 and 2012, RVS-assisted PTBD was performed for 30 patients (19 males and 11 females; age range, 41 to 89 years; mean age, 66.9 years) with obstructive jaundice. The targeted bile duct was determined using the RVS system before the procedure. The intervention was considered to be successful when the targeted bile duct was punctured and the drainage catheter was placed in the bile duct. Complications were evaluated according to the Society of Interventional Radiology Clinical Practice Guidelines.ResultsA total of 37 interventions were performed for 30 patients. The interventions were successful in 35 (95%) of 37 interventions. The targeted bile ducts were: B3 (n = 24), B5 (n = 7), B8 (n = 3), B6 (n = 1), and the anterior (n = 1) and posterior (n = 1) branches of the right bile duct. The mean targeted bile duct diameter was 4.9 mm (1.9 to 8.2 mm). PTBD was able to be accomplished in all patients because the non-targeted bile ducts were successfully punctured alternatively. No major complications were observed in relation to the interventional procedure.ConclusionsRVS-assisted PTBD is a feasible and safe procedure. Accurate puncture of targeted bile ducts can be achieved using this method.

A Soft Computing Decision Support System in the Diagnosis of Breast Cancer

is well known that most of Breast cancer diagnosis characterization processes are entirely based on physician's intuition and experience. Since diagnosis of breast cancer involves several layers of uncertainty and imprecision that makes traditional approaches inappropriate. In the present research paper a soft computing diagnostic support system for breast cancer is proposed which is capable enough to capture ambiguous and imprecise information prevalent in breast cancer diagnosis. It is user friendly and will sharpen diagnostic skill of medical practitioners. KeywordsComputing, Breast Cancer, Fuzzy Tools

Neuroimaging-aided differential diagnosis of the depressive state

A serious problem in psychiatric practice is the lack of specific, objective biomarker-based assessments to guide diagnosis and treatment. The use of such biomarkers could assist clinicians in establishing differential diagnosis, which may improve specific individualised treatment. This multi-site study sought to develop a clinically suitable neuroimaging-guided diagnostic support system for differential diagnosis at the single-subject level among multiple psychiatric disorders with depressive symptoms using near-infrared spectroscopy, which is a compact and portable neuroimaging method. We conducted a multi-site, case–control replication study using two cohorts, which included seven hospitals in Japan. The study included 673 patients (women/men: 315/358) with psychiatric disorders (major depressive disorder, bipolar disorder, or schizophrenia) who manifested depressive symptoms, and 1007 healthy volunteers (530/477). We measured the accuracy of the single-subject classification in differential diagnosis among major psychiatric disorders, based on spatiotemporal characteristics of fronto-temporal cortical haemodynamic response patterns induced by a brief (<3min) verbal fluency task. Data from the initial site were used to determine an optimal threshold, based on receiver-operator characteristics analysis, and to generate the simplest and most significant algorithm, which was validated using data from the remaining six sites. The frontal haemodynamic patterns detected by the near-infrared spectroscopy method accurately distinguished between patients with major depressive disorder (74.6%) and those with the two other disorders (85.5%; bipolar disorder or schizophrenia) that presented with depressive symptoms. These results suggest that neuroimaging-guided differential diagnosis of major psychiatric disorders developed using the near-infrared spectroscopy method can be a promising biomarker that should aid in personalised care in real clinical settings. Potential confounding effects of clinical (e.g., age, sex) and systemic (e.g., autonomic nervous system indices) variables on brain signals will need to be clarified to improve classification accuracy.

Feature selection for medical diagnosis : Evaluation for cardiovascular diseases

Machine learning has emerged as an effective medical diagnostic support system. In a medical diagnosis problem, a set of features that are representative of all the variations of the disease are necessary. The objective of our work is to predict more accurately the presence of cardiovascular disease with reduced number of attributes. We investigate intelligent system to generate feature subset with improvement in diagnostic performance. Features ranked with distance measure are searched through forward inclusion, forward selection and backward elimination search techniques to find subset that gives improved classification result. We propose hybrid forward selection technique for cardiovascular disease diagnosis. Our experiment demonstrates that this approach finds smaller subsets and increases the accuracy of diagnosis compared to forward inclusion and back-elimination techniques.

Computer aided diagnostic support system for skin cancer: a review of techniques and algorithms.

Image-based computer aided diagnosis systems have significant potential for screening and early detection of malignant melanoma. We review the state of the art in these systems and examine current practices, problems, and prospects of image acquisition, pre-processing, segmentation, feature extraction and selection, and classification of dermoscopic images. This paper reports statistics and results from the most important implementations reported to date. We compared the performance of several classifiers specifically developed for skin lesion diagnosis and discussed the corresponding findings. Whenever available, indication of various conditions that affect the technique's performance is reported. We suggest a framework for comparative assessment of skin cancer diagnostic models and review the results based on these models. The deficiencies in some of the existing studies are highlighted and suggestions for future research are provided.

1A2-B11 CFRP複合材を用いた超音波診断・治療補助ロボットReDATの開発(医療ロボティクス・メカトロニクス(2))

Among various medical problems facing us in today's Japan are rapidly aging society, localization of medical doctors, concentrated visits of patients to large hospitals, etc. Development of diagnostic support systems is considered a current pressing solution to help reduce loads on medical doctors. On the other hand, actual diagnosis is used various patient positions such as supine posture and lying posture. So, in this paper, we purpose the development of the ultrasound diagnosis support robot system to be able to diagnose by various patient position. It is needed to workings high accuracy, high-speed and workings by extending to the large range to some degree. We have designed 10 DOF (degrees of freedom) redundant manipulators for ultrasound diagnostic and treatment support. Also we assumed material of robot is used Aluminum or Carbon Fiber Reinforced Plastics Matrix-composite, and we compared robot used CFRP with robot used Aluminum in total weight and max stress. Then, the adjustable link system that is used fuzzy inference has been constructed and its availability has been verified. And the model of 5-DOF manipulator has been simulated with the system. Then, pneumatic cylinder and electrical actuator control experiment has been conducted to verify the effectiveness of the system.

Characteristics of progressive multifocal leukoencephalopathy clarified through internet-assisted laboratory surveillance in Japan

BackgroundProgressive multifocal leukoencephalopathy (PML), a rare but fatal demyelinating disease caused by JC virus (JCV), occurs mainly in immunocompromised patients. As PML develops in individuals with various underlying disorders sporadically and infrequently, a nationwide survey of PML is difficult. This study was conducted to elucidate the characteristics of PML in Japan through an internet-assisted laboratory surveillance program.MethodsA diagnostic support system for PML was established using a real-time PCR assay of JCV DNA in cerebrospinal fluid (CSF), and requests for testing were received from clinicians via specialized websites. Medical histories of patients were collected through standardized questionnaires, and a database of CSF JCV loads and clinical information was created and analyzed.ResultsFor 4 years from April 2007 to March 2011, CSF specimens from 419 patients were tested. Forty-eight individuals were found positive for JCV DNA in their CSF and were diagnosed with PML. PML primarily occurred not only in HIV-positive patients (33.3%) but also in patients with hematologic disorders after receiving stem cell transplantation, chemotherapy, and/or immunosuppressive treatment (39.6%). The frequencies of PML cases among the subjects in these two categories were 20.3% and 23.5%, respectively. Although no significant features were observed with respect to CSF JCV loads in PML patients with an HIV infection or hematologic disorder, males were predominant in both groups (100% and 89.5%, respectively). The proportion of PML cases with autoimmune disorders (6.3%) or solid-organ transplants (2.1%) was smaller than those with HIV infection or hematologic disorders, probably due to the limited availability of therapeutic monoclonal antibodies and transplantation from brain dead donors.ConclusionsThe results suggest that the internet-assisted laboratory surveillance program might be a useful strategy for collecting precise real-time information on PML on a national level. The current database provides important background information for the diagnosis and treatment of patients with risk factors for PML.

Identification of atrophy of the subgenual anterior cingulate cortex, in particular the subcallosal area, as an effective auxiliary means of diagnosis for major depressive disorder

BackgroundDespite being a very common psychiatric disorder, physicians often have difficulty making a diagnosis of major depressive disorder (MDD) because, without established diagnostic criteria, they have to depend on interviews with patients and observation to assess psychiatric symptoms. However, previous researchers have reported that magnetic resonance imaging (MRI) scans identify morphological changes in the brains of patients with MDD, which inspired us to hypothesize that assessment of local changes in the brain using voxel-based morphometry would serve as an auxiliary diagnostic method for MDD. Therefore, we focused on the VSRAD® plus (voxel-based specific regional analysis system for Alzheimer’s disease), a diagnostic support system for use in early Alzheimer’s disease, which allowed us to identify regional atrophy in the brain easily based on images obtained from MRI scans.MethodsThe subjects were 75 patients with MDD, 15 with bipolar disorder, and 30 healthy subjects, aged 54–82 years. First, 1.5 T MRI equipment was used to scan three-dimensional T1-weighted images for the individual subjects, and the imaged data were analyzed by VSRAD advance (voxel-based morphometric software developed for diagnosis of early Alzheimer’s disease). The efficacy of the equipment for diagnosis of MDD was evaluated based on the distribution of atrophy in the subgenual anterior cingulate cortex (sACC) on the z-score map obtained.ResultsNo significant difference in atrophy was noted between the left and right sACCs. The VSRAD advance used in the present study was more effective than the VSRAD plus for diagnosis of MDD, with a sensitivity of 90.7%, specificity of 86.7%, accuracy of 89.5%, a positive predictive value of 94.4%, and a negative predictive value of 78.8%. In particular, atrophy was observed in the subcallosal area of the sACC.ConclusionThe identification of atrophy in the sACC, in particular of the subcallosal area, with the use of updated voxel-based morphometric software proved to be effective as an auxiliary diagnostic method for MDD.

Bayesian ontology querying for accurate and noise-tolerant semantic searches

Ontologies provide a structured representation of the concepts of a domain of knowledge as well as the relations between them. Attribute ontologies are used to describe the characteristics of the items of a domain, such as the functions of proteins or the signs and symptoms of disease, which opens the possibility of searching a database of items for the best match to a list of observed or desired attributes. However, naive search methods do not perform well on realistic data because of noise in the data, imprecision in typical queries and because individual items may not display all attributes of the category they belong to. We present a method for combining ontological analysis with Bayesian networks to deal with noise, imprecision and attribute frequencies and demonstrate an application of our method as a differential diagnostic support system for human genetics. We provide an implementation for the algorithm and the benchmark at http://compbio.charite.de/boqa/. Sebastian.Bauer@charite.de or Peter.Robinson@charite.de Supplementary Material for this article is available at Bioinformatics online.

Why Do Patients Derogate Physicians Who Use a Computer-Based Diagnostic Support System?

To better understand 1) why patients have a negative perception of the use of computerized clinical decision support systems (CDSSs) and 2) what contributes to the documented heterogeneity in the evaluations of physicians who use a CDSS. Three vignette-based studies examined whether negative perceptions stemmed directly from the use of a computerized decision aid or the need to seek external advice more broadly (experiment 1) and investigated the contributing role of 2 individual difference measures, attitudes toward statistics (ATS; experiment 2) and the Multidimensional Health Locus of Control Scale (MHLC; experiment 3), to these findings. A physician described as making an unaided diagnosis was rated significantly more positively on a number of attributes than a physician using a computerized decision aid but not a physician who sought the advice of an expert colleague (experiment 1). ATS were unrelated to perceptions of decision aid use (experiment 2); however, greater internal locus of control was associated with more positive feelings about unaided care and more negative feelings about care when a decision aid was used (experiment 3). Negative perceptions of computerized decision aid use may not be a product of the need to seek external advice more generally but may instead be specific to the use of a nonhuman tool and may be associated with individual differences in locus of control. Together, these 3 studies may be used to guide education efforts for patients.