Kaya Kuru

Biomedical visual data analysis to build an intelligent diagnostic decision support system in medical genetics

BACKGROUND In general, medical geneticists aim to pre-diagnose underlying syndromes based on facial features before performing cytological or molecular analyses where a genotype-phenotype interrelation is possible. However, determining correct genotype-phenotype interrelationships among many syndromes is tedious and labor-intensive, especially for extremely rare syndromes. Thus, a computer-aided system for pre-diagnosis can facilitate effective and efficient decision support, particularly when few similar cases are available, or in remote rural districts where diagnostic knowledge of syndromes is not readily available. METHODS The proposed methodology, visual diagnostic decision support system (visual diagnostic DSS), employs machine learning (ML) algorithms and digital image processing techniques in a hybrid approach for automated diagnosis in medical genetics. This approach uses facial features in reference images of disorders to identify visual genotype-phenotype interrelationships. Our statistical method describes facial image data as principal component features and diagnoses syndromes using these features. RESULTS The proposed system was trained using a real dataset of previously published face images of subjects with syndromes, which provided accurate diagnostic information. The method was tested using a leave-one-out cross-validation scheme with 15 different syndromes, each of comprised 5-9 cases, i.e., 92 cases in total. An accuracy rate of 83% was achieved using this automated diagnosis technique, which was statistically significant (p<0.01). Furthermore, the sensitivity and specificity values were 0.857 and 0.870, respectively. CONCLUSION Our results show that the accurate classification of syndromes is feasible using ML techniques. Thus, a large number of syndromes with characteristic facial anomaly patterns could be diagnosed with similar diagnostic DSSs to that described in the present study, i.e., visual diagnostic DSS, thereby demonstrating the benefits of using hybrid image processing and ML-based computer-aided diagnostics for identifying facial phenotypes.

A novel report generation approach for medical applications: The SISDS methodology and its applications

BACKGROUND Despite exciting innovation in information system technologies, the medical reporting has remained static for a long time. Structured reporting was established to address the deficiencies in report content but has largely failed in its adoption due to concerns over workflow and productivity. The methods used in medical reporting are insufficient in providing with information for statistical processing and medical decision making as well as high quality healthcare. OBJECTIVE The aim of this study is to introduce a novel method that enables professionals to efficiently produce medical reports that are less error-prone and can be used in decision support systems without extensive post-processing. METHODOLOGY We first present the formal definition of the proposed method, called SISDS, that provides a clear separation between the data, logic and presentation layers. It allows free-text like structured data entry in a structured form, and reduces the cognitive effort by inline editing and dynamically controlling the information flow based on the entered data. Then, we validate the usability and reliability of the method on a real-world testbed in the field of radiology. For this purpose, a sample esophagus report was constructed by a focus group of radiologists and real patient data have been collected using a web-based prototype; these data are then used to build a decision support system with off-the-shelf tools. The usability of the method is assessed by evaluating its acceptability by the users and the accuracy of the resulting decision support system. For reliability, we conducted a controlled experiment comparing the performance of the method to that of transcriptionist-oriented systems in terms of the rate of successful diagnosis and the total time required to enter the data. RESULT The most noticeable observation in the evaluation is that the rate of successful diagnosis improves significantly with the proposed method; in our case study, a success rate of 81.25% has been achieved by using the SISDS method compared to 43.75% for the transcriptionist-oriented system. In addition, the average time required to obtain the final approved reports decreased from 29 min to 14 min. Based on questionnaire responses, the acceptance rate of the SISDS methodology by users is also found to outperform the rates of the current methods. CONCLUSION The empirical results show that the method can effectively help to reduce medical errors, increase data quality, and lead to more accurate decision support. In addition, the dynamic hierarchical data entry model proves to provide a good balance between cognitive load and structured data collection.

Optimization and enhancement of H&E stained microscopical images by applying bilinear interpolation method on lab color mode

BackgroundHematoxylin & Eosin (H&E) is a widely employed technique in pathology and histology to distinguish nuclei and cytoplasm in tissues by staining them in different colors. This procedure helps to ease the diagnosis by enhancing contrast through digital microscopes. However, microscopic digital images obtained from this technique usually suffer from uneven lighting, i.e. poor Koehler illumination. Several off-the-shelf methods particularly established to correct this problem along with some popular general commercial tools have been examined to find out a robust solution.MethodsFirst, the characteristics of uneven lighting in pathological images obtained from the H&E technique are revealed, and then how the quality of these images can be improved by employing bilinear interpolation based approach applied on the channels of Lab color mode is explored without losing any essential detail, especially for the color information of nuclei (hematoxylin stained sections). Second, an approach to enhance the nuclei details that are a fundamental part of diagnosis and crucially needed by the pathologists who work with digital images is demonstrated.ResultsMerits of the proposed methodology are substantiated on sample microscopic images. The results show that the proposed methodology not only remedies the deficiencies of H&E microscopical images, but also enhances delicate details.ConclusionsNon-uniform illumination problems in H&E microscopical images can be corrected without compromising crucial details that are essential for revealing the features of tissue samples.

A bilinear interpolation based approach for optimizing hematoxylin and eosin stained microscopical images

Hematoxylin & Eosin (HE this helps to ease the diagnosis process. However, usually the microscopic digital images obtained using this technique suffer from uneven lighting, i.e. poor Koehler illumination. The existing ad-hoc methods for correcting this problem generally work in RGB color model, and may result in both an unwanted color shift and loosing essential details in terms of the diagnosis. The aim of this study is to present an alternative method that remedies these deficiencies. We first identify the characteristics of uneven lighting in pathological images produced by using the H&E technique, and then show how the quality of these images can be improved by applying an interpolation based approach in the Lab color model without losing any important detail. The effectiveness of the proposed method is demonstrated on sample microscopic images.

Flight Guardian: Autonomous Flight Safety Improvement by Monitoring Aircraft Cockpit Instruments

During in-flight emergencies, a pilot’s workload increases significantly, and it is often during this period of increased stress that human errors occur that consequently diminish the flight safety...

An Intelligent System for Spoken Term Detection That Uses Belief Combination

Spoken term detection (STD) can be considered a subpart of automatic speech recognition that aims to extract partial information from speech signals in the form of query utterances. A variety of STD techniques available in the literature employ a single source of evidence for query utterance match/mismatch determination. In this article, the authors develop an acoustic signal processing-based approach for STD that incorporates several techniques for silence removal, dynamic noise filtration, and evidence combination using the Dempster-Shafer theory. Spectral-temporal features-based voiced segment detection and energy and zero cross rate-based unvoiced segment detection remove silence segments in the speech signal. Comprehensive experiments have been performed on large speech datasets and satisfactory results have been achieved with the proposed approach, which improves existing speaker-dependent STD approaches, specifically the reliability of query utterance spotting, by combining the results from multiple belief sources.

A Novel Hybrid Clustering Approach for Unsupervised Grouping of Similar Objects

A novel hybrid clustering methodology named CDFISM (Clustering Distinct Features in Similarity Matrix) for grouping of similar objects is implemented in this study to address the unsatisfactory clustering results of current methods. Well-known PCA and a distance measuring method along with a new established algorithm (CISM) are employed to establish CDFISM methodology. CISM embodies both Rk-means method and an agglomerative/contractive/expansive (ACE) method. The CDFISM methodology has been tested on sample face images in three face databases to ensure the viability of the methodology. A high rate of accuracy has been achieved with the methodology, namely 97.5%, 98.75% and 80% respectively regarding the three image databases used in the study, averaging 92%. The hybrid methodology runs effectively for revealing interrelated pattern of similarities among objects.

Establishment of a Diagnostic Decision Support System in Genetic Dysmorphology

In the clinical diagnosis of facial dysmorphology, geneticists attempt to identify the underlying syndromes by associating facial features before cyto or molecular techniques are explored. Specifying genotype-phenotype correlations correctly among many syndromes is labor intensive especially for very rare diseases. The use of a computer based prediagnosis system can offer effective decision support particularly when only very few previous examples exist or in a remote environment where expert knowledge is not readily accessible. In this work we develop and demonstrate that accurate classification of dysmorphic faces is feasible by image processing of two dimensional face images. We test the proposed system on real patient image data by constructing a dataset of dysmorphic faces published in scholarly journals, hence having accurate diagnostic information about the syndrome. Our statistical methodology represents facial image data in terms of principal component analysis (PCA) and a leave one out evaluation scheme to quantify accuracy. The methodology has been tested with 15 syndromes including 75 cases, 5 examples per syndrome. A diagnosis success rate of 79% has been established. It can be concluded that a great number of syndromes indicating a characteristic pattern of facial anomalies can be typically diagnosed by employing computer-assisted machine learning algorithms since a face develops under the influence of many genes, particularly the genes causing syndromes.

Developing Diagnostic DSSs Based on a Novel Data Collection Methodology

Although necessary information on prognostic implications is missing and reliable data are available in very few areas of medicine, there is an increasing demand for diagnostic decision support systems (DDSS), mainly due to the multitude of variables involved and highly complex relations between them. Unfortunately, existing approaches seem inadequate for providing accurate and high quality data --- a prerequisite for establishing a successful DDSS. In this paper, we demonstrate how SISDS methodology that aims to remedy the deficiencies of current systems in use can be utilized to ease the data collection process and provide opportunities to construct DDSSs without tedious pre-processing and data preparation steps. We also provide empirical results on a real-world testbed application in the field of radiology.

A Novel Multilingual Report Generation System for Medical Applications

There has been an increasing demand for high quality medical data that are in a standard electronic format and easily shared. Although a great amount of effort has been invested to ease the process, an effective solution has yet to be found. In this study, we first discuss necessary features of an effective data collection and reporting system, and then reveal the conceptual view of a novel method that aims to encompass these features. We also present the design and implementation details of a Web-based prototype.