A decision support system for Crithidia luciliae image classification

A computer-aided diagnosis system for HEp-2 fluorescence intensity classification

BACKGROUND AND OBJECTIVE

The indirect immunofluorescence (IIF) on HEp-2 cells is the recommended technique for the detection of antinuclear antibodies. However, it is burdened by some limitations, as it is time consuming and subjective, and it requires trained personnel. In other fields the adoption of deep neural networks has provided an effective high-level abstraction of the raw data, resulting in the ability to automatically generate optimized high-level features.

METHODS

To alleviate IIF limitations, this paper presents a computer-aided diagnosis (CAD) system classifying HEp-2 fluorescence intensity: it represents each image using an Invariant Scattering Convolutional Network (Scatnet), which is locally translation invariant and stable to deformations, a characteristic useful in case of HEp-2 samples. To cope with the inter-observer discrepancies found in the dataset, we also introduce a method for gold standard computation that assigns a label and a reliability score to each HEp-2 sample on the basis of annotations provided by expert physicians. Features by Scatnet and gold standard information are then used to train a Support Vector Machine.

RESULTS

The proposed CAD is tested on a new dataset of 1771 images annotated by three independent medical centers. The performances achieved by our CAD in recognizing positive, weak positive and negative samples are also compared against those obtained by other two approaches presented so far in the literature. The same system trained on this new dataset is then tested on two public datasets, namely MIVIA and I3Asel.

CONCLUSIONS

The results confirm the effectiveness of our proposal, also revealing that it achieves the same performance as medical experts.

A decision support system for type 1 diabetes mellitus diagnostics based on dual channel analysis of red blood cell membrane fluidity

BACKGROUND AND OBJECTIVE

Investigation of membrane fluidity by metabolic functional imaging opens up a new and important area of translational research in type 1 diabetes mellitus, being a useful and sensitive biomarker for disease monitoring and treatment. We investigate here how data on membrane fluidity can be used for diabetes monitoring.

METHODS

We present a decision support system that distinguishes between healthy subjects, type 1 diabetes mellitus patients, and type 1 diabetes mellitus patients with complications. It leverages on dual channel data computed from the physical state of human red blood cells membranes by means of features based on first- and second-order statistical measures as well as on rotation invariant co-occurrence local binary patterns. The experiments were carried out on a dataset of more than 1000 images belonging to 27 subjects.

RESULTS

Our method shows a global accuracy of 100%, outperforming also the state-of-the-art approach based on the glycosylated hemoglobin.

CONCLUSIONS

The proposed recognition approach permits to achieve promising results.

Performance analysis of automated evaluation of Crithidia luciliae-based indirect immunofluorescence tests in a routine setting - strengths and weaknesses

BACKGROUND

Antibodies directed against dsDNA are a highly specific diagnostic marker for the presence of systemic lupus erythematosus and of particular importance in its diagnosis. To assess anti-dsDNA antibodies, the Crithidia luciliae-based indirect immunofluorescence test (CLIFT) is one of the assays considered to be the best choice. To overcome the drawback of subjective result interpretation that inheres indirect immunofluorescence assays in general, automated systems have been introduced into the market during the last years. Among these systems is the EUROPattern Suite, an advanced automated fluorescence microscope equipped with different software packages, capable of automated pattern interpretation and result suggestion for ANA, ANCA and CLIFT analysis.

METHODS

We analyzed the performance of the EUROPattern Suite with its automated fluorescence interpretation for CLIFT in a routine setting, reflecting the everyday life of a diagnostic laboratory. Three hundred and twelve consecutive samples were collected, sent to the Central Diagnostic Laboratory of the Maastricht University Medical Centre with a request for anti-dsDNA analysis over a period of 7 months.

RESULTS

Agreement between EUROPattern assay analysis and the visual read was 93.3%. Sensitivity and specificity were 94.1% and 93.2%, respectively. The EUROPattern Suite performed reliably and greatly supported result interpretation.

CONCLUSIONS

Automated image acquisition is readily performed and automated image classification gives a reliable recommendation for assay evaluation to the operator. The EUROPattern Suite optimizes workflow and contributes to standardization between different operators or laboratories.

The inter-observer reading variability in anti-nuclear antibodies indirect (ANA) immunofluorescence test: A multicenter evaluation and a review of the literature

Recently there has been an increase demand for Computer-Aided Diagnosis (CAD) tools to support clinicians in the field of Indirect ImmunoFluorescence (IIF), as the novel digital imaging reading approach can help to overcome the reader subjectivity. Nevertheless, a large multicenter evaluation of the inter-observer reading variability in this field is still missing. This work fills this gap as we evaluated 556 consecutive samples, for a total of 1679 images, collected in three laboratories with IIF expertise using HEp-2 cell substrate (MBL) at 1:80 screening dilution according to conventional procedures. In each laboratory, the images were blindly classified by two experts into three intensity classes: positive, negative, and weak positive. Positive and weak positive ANA-IIF results were categorized by the predominant fluorescence pattern among six main classes. Data were pairwise analyzed and the inter-observer reading variability was measured by Cohen's kappa test, revealing a pairwise agreement little further away than substantial both for fluorescence intensity and for staining pattern recognition (k=0.602 and k=0.627, respectively). We also noticed that the inter-observer reading variability decreases when it is measured with respect to a gold standard classification computed on the basis of labels assigned by the three laboratories. These data show that laboratory agreement improves using digital images and comparing each single human evaluation to potential reference data, suggesting that a solid gold standard is essential to properly make use of CAD systems in routine work lab.

Detection of anti-dsDNA antibodies by computer-aided automated immunofluorescence analysis

INTRODUCTION

NOVA View is a computer aided fluorescence microscope that is used for the automated reading and interpretation of indirect immunofluorescent tests in diagnostic immunology. The objective of the present study was to evaluate the performance of the NOVA View® system for the measurement of anti-dsDNA antibodies using the Crithidia luciliae indirect immunofluorescence test (CLIFT) technology.

METHODS

Analytical performance of NOVA View CLIFT was assessed in repeatability (within run) and reproducibility (between runs and instruments) studies. Two hundred-fifty patient samples (N=200 consecutive samples and N=50 samples from systemic lupus erythematosus patients) were tested to evaluate the agreement between results generated with NOVA View CLIFT, and those obtained with manual microscopic reading of the same slides. Positivity rate in SLE was assessed on the 50 SLE samples.

RESULTS

The NOVA View system showed high level of repeatability and reproducibility within runs, between runs, and between instruments. Agreement of NOVA View software interpretation and digital image reading results with manual microscopic reading results was 96.0%, and the same positivity rate was obtained on SLE samples by NOVA View digital image reading as that of manual microscopic reading (36.0% vs. 38.0%, respectively).

CONCLUSION

Results generated by NOVA View CLIFT were equivalent to those obtained by manual microscopic reading on a large routine sample set. NOVA View demonstrated consistency within and between runs, and between instruments. Automation of CLIFT provides reliability and is a suitable alternative for routine clinical laboratories.

Applying artificial intelligence technology to support decision-making in nursing: A case study in Taiwan

This study applied artificial intelligence to help nurses address problems and receive instructions through information technology. Nurses make diagnoses according to professional knowledge, clinical experience, and even instinct. Without comprehensive knowledge and thinking, diagnostic accuracy can be compromised and decisions may be delayed. We used a back-propagation neural network and other tools for data mining and statistical analysis. We further compared the prediction accuracy of the previous methods with an adaptive-network-based fuzzy inference system and the back-propagation neural network, identifying differences in the questions and in nurse satisfaction levels before and after using the nursing information system. This study investigated the use of artificial intelligence to generate nursing diagnoses. The percentage of agreement between diagnoses suggested by the information system and those made by nurses was as much as 87 percent. When patients are hospitalized, we can calculate the probability of various nursing diagnoses based on certain characteristics.

Automated Evaluation of Crithidia luciliae Based Indirect Immunofluorescence Tests: A Novel Application of the EUROPattern-Suite Technology

Systemic lupus erythematosus (SLE) is a severe rheumatic autoimmune disease with various clinical manifestations. Anti-dsDNA antibodies are an important immunological hallmark of SLE and their occurrence represents a major criterion for the diagnosis. Among the commonly applied test systems for determination of anti-dsDNA antibodies, the indirect immunofluorescence test (IIFT) using the flagellated kinetoplastida Crithidia luciliae is considered to be highly disease specific at moderate sensitivity. Since IIFT, however, is claimed to be affected by subjective interpretation and a lack of standardization, there has been an increasing demand for automated pattern interpretation of immunofluorescence reactions in recent years. Corresponding platforms are already available for evaluation of anti-nuclear antibody (ANA) IIFT on HEp-2 cells, the recommended “gold standard” for ANA screening in the diagnosis of various systemic rheumatic autoimmune diseases. For one of these systems, the “EUROPattern-Suite” computer-aided immunofluorescence microscopy (CAIFM), automated interpretation of microscopic fluorescence patterns was extended to the Crithidia luciliae based anti-dsDNA IIFT.

Cancer diagnosis by nuclear morphometry using spatial information

Methods for extracting quantitative information regarding nuclear morphology from histopathology images have been long used to aid pathologists in determining the degree of differentiation in numerous malignancies. Most methods currently in use, however, employ the naïve Bayes approach to classify a set of nuclear measurements extracted from one patient. Hence, the statistical dependency between the samples (nuclear measurements) is often not directly taken into account. Here we describe a method that makes use of statistical dependency between samples in thyroid tissue to improve patient classification accuracies with respect to standard naïve Bayes approaches. We report results in two sample diagnostic challenges.

New platform technology for comprehensive serological diagnostics of autoimmune diseases

Antibody assessment is an essential part in the serological diagnosis of autoimmune diseases. However, different diagnostic strategies have been proposed for the work up of sera in particular from patients with systemic autoimmune rheumatic disease (SARD). In general, screening for SARD-associated antibodies by indirect immunofluorescence (IIF) is followed by confirmatory testing covering different assay techniques. Due to lacking automation, standardization, modern data management, and human bias in IIF screening, this two-stage approach has recently been challenged by multiplex techniques particularly in laboratories with high workload. However, detection of antinuclear antibodies by IIF is still recommended to be the gold standard method for antibody screening in sera from patients with suspected SARD. To address the limitations of IIF and to meet the demand for cost-efficient autoantibody screening, automated IIF methods employing novel pattern recognition algorithms for image analysis have been introduced recently. In this respect, the AKLIDES technology has been the first commercially available platform for automated interpretation of cell-based IIF testing and provides multiplexing by addressable microbead immunoassays for confirmatory testing. This paper gives an overview of recently published studies demonstrating the advantages of this new technology for SARD serology.