Computer-assisted classification of HEp-2 immunofluorescence patterns in autoimmune diagnostics

Artificial intelligence applications for immunology laboratory: image analysis and classification study of IIF photos

Artificial intelligence (AI) is increasingly being used in medicine to enhance the speed and accuracy of disease diagnosis and treatment. AI-based image analysis is expected to play a crucial role in future healthcare facilities and laboratories, offering improved precision and cost-effectiveness. As technology advances, the requirement for specialized software knowledge to utilize AI applications is diminishing. Our study will examine the advantages and challenges of employing AI-based image analysis in the field of immunology and will investigate whether physicians without software expertise can use MS Azure Portal for ANA IIF test classification and image analysis. This is the first study to perform Hep-2 image analysis using MS Azure Portal. We will also assess the potential for AI applications to aid physicians in interpreting ANA IIF results in immunology laboratories. The study was designed in four stages by two specialists. Stage 1: creation of an image library, Stage 2: finding an artificial intelligence application, Stage 3: uploading images and training artificial intelligence, Stage 4: performance analysis of the artificial intelligence application. In the first training, the average pattern identification accuracy for 72 testing images was 81.94%. After the second training, this accuracy increased to 87.5%. Patterns Precision improved from 71.42 to 79.96% after the second training. As a result, the number of correctly identified patterns and their accuracy increased with the second training process. Artificial intelligence-based image analysis shows promising potential. This technology is expected to become essential in healthcare facility laboratories, offering higher accuracy rates and lower costs.

Machine learning, a new tool for the detection of immunodeficiency patterns in systemic lupus erythematosus

Systemic lupus erythematosus (SLE) is a complex autoimmune disease that affects several organs and causes variable clinical symptoms. Early diagnosis is currently the most effective way to save the lives of patients with SLE. But it is very difficult to detect in the early stages of the disease. Because of this, this study proposes a machine learning system to help diagnose patients with SLE. To carry out the research, the extreme gradient boosting method has been implemented due to its performance characteristics, as it allows high performance, scalability, accuracy, and low computational load. From this method we try to recognize patterns in the data obtained from patients, which allow the classification of SLE patients with high accuracy and differentiate these patients from controls. Several machine learning methods have been analyzed in this study. The proposed method achieves a higher prediction value of patients who may suffer from SLE than the rest of the compared systems. The proposed algorithm achieved an improvement in accuracy of 4.49% over k-Nearest Neighbors. As for the Support Vector Machine and Gaussian Naive Bayes (GNB) methods, they achieved a lower performance than the proposed one, reaching values of 83% and 81%, respectively. It should be noted that the proposed system showed a higher area under the curve (90%) and a balanced accuracy (90%) than the other machine learning methods. This study shows the usefulness of ML techniques for identifying and predicting SLE patients. These results demonstrate the possibility of developing automatic diagnostic support systems for SLE patients based on machine learning techniques.

An indirect comparison of automated indirect immunofluorescence vs automated solid-phase immunoassays for antinuclear antibody detection: A meta-analysis and adjusted indirect comparison of diagnostic test accuracy

OBJECTIVE

The primary aim of this study was to conduct a meta-analysis to compare the diagnostic accuracy of automated indirect immunofluorescence (automated-IIF) and fully automated solid-phase immunoassays (solid-phase assays), compared with gold standard conventional manual indirect immunofluorescence (manual-IIF) for antinuclear antibody (ANA) detection.

METHODS

Indirect meta-comparison was performed using prospective studies reporting comparative data between automated-IIF and fully automated solid-phase assays individually to conventional manual-IIF. Diagnostic tests regarding different automated solid-phase assays and automated-IIF for ANA detection were retrieved from the Cochrane Library, PubMed, Embase, Web of Science, Chinese Biological Medicine Database (CBM), China National Knowledge Infrastructure (CNKI), and WANFANG electronic databases from their inception to January 2022. Assessment of the quality of the studies was undertaken using a second version of the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. The investigated diagnostic indices including pooled sensitivity, pooled specificity, pooled positive likelihood ratio (PLR), pooled negative likelihood ratio (NLR), pooled diagnostic odds ratio (DOR), area under the summary receiver operating characteristic (AUC) of automated-IIF and solid-phase assays, respectively. Relative diagnostic odds ratio (RDOR) was calculated to indirectly compare the diagnostic accuracy of automated-IIF and solid-phase assays. To visualize results, we provide forest plots showing the RDOR with 95% confidence intervals (CI) of the 2 methods against the "gold standard" manual-IIF by R software. Deeks' funnel was used to investigate the publication bias.

RESULTS

A total of 16 studies involving 6111 subjects were included in the analysis. The pooled sensitivity, pooled specificity, pooled PLR, pooled NLR, pooled DOR and the AUC were 0.85 (95% CI: 0.84-0.86), 0.82 (95% CI 0.81-0.84), 14.22 (95% CI 8.55-23.65), 0.06 (95% CI 0.03-0.12), 287.0 (95% CI 124.30-662.68) and 0.983 for automated-IIF respectively, and as for solid-phase assays those were 0.73 (95% CI 0.70-0.75), 0.87 (95% CI 0.85-0.89), 5.66 (95% CI 3.33-9.62), 0.30 (95% CI 0.20-0.47), 19.14 (95% CI 8.00-45.79) and 0.894. The results of indirect comparison indicated that automated-IIF had statistically significant higher accuracy for the detection of ANA.

CONCLUSION

This meta-analysis and indirect comparison suggest that automated-IIF should be recommended as an alternative assay for ANA screening under the condition of increased demand for ANA testing in clinical immunology laboratories.

Heterogeneous ensemble with information theoretic diversity measure for human epithelial cell image classification

In this work, we propose a heterogeneous committee (ensemble) of diverse members (classification approaches) to solve the problem of human epithelial (HEp-2) cell image classification using indirect Immunofluorescence (IIF) imaging. We hypothesize that an ensemble involving different feature representations can enable higher performance if individual members in the ensemble are sufficiently varied. These members are of two types: (1) CNN-based members, (2) traditional members. For the CNN members, we have employed the well-established ResNet, DenseNet, and Inception models, which have distinctive salient aspects. For the traditional members, we incorporate class-specific features which are characterized depending on visual morphological attributes, and some standard texture features. To select the members which are discriminating and not redundant, we use an information theoretic measure which considers the trade-off between individual accuracies and diversity among the members. For all selected members, a compelling fusion required to combine their outputs to reach a final decision. Thus, we also investigate various fusion methods that combine the opinion of the committee at different levels: maximum voting, product, decision template, Bayes, Dempster-Shafer, etc. The proposed method is evaluated using ICPR-2014 data which consists of more images than some previous datasets ICPR-2012 and demonstrate state-of-the-art performance. To check the effectiveness of the proposed methodology for other related datasets, we test our methodology with newly compiled large-scale HEp-2 dataset with 63K cell images and demonstrate comparable performance even with less number of training samples. The proposed method produces 99.80% and 86.03% accuracy respectively when tested on ICPR-2014 and a new large-scale data containing 63K samples. Graphical Abstract Overview of the proposed methodology.

Integrating quality assurance in autoimmunity: the changing face of the automated ANA IIF test

OBJECTIVES

Currently available computer-aided diagnosis (CAD) systems for the detection of anti-nuclear antibodies (ANA) by indirect immunofluorescence (IIF) assay enable a standardized measurement of system-specific fluorescent intensity (FI) measures. We aimed to evaluate an internal quality control (iQC) program that controls the total ANA IIF process in routine practice.

METHODS

In addition to the kit iQC materials, supplemental quality indicators were integrated in a total quality assurance (QA) program: patient-derived iQC's samples (negative, 1/160 fine speckled and 1/160 homogeneous), median sample FI per run and percentage of ANA IIF positive samples per run. Analytical rejection criteria were based on the imprecision of the positivity index (PI) measure of the Zenit PRO system (Menarini). Clinical rejection criteria were based on changes in FI that correspond to a change in ANA IIF titer of ≥2. To evaluate the QA program, different artificial errors were introduced during the ANA IIF process. After every run, quality indicators were evaluated and compared to the pre-set target values.

RESULTS

Rescanning the ANA IIF slides five times, using an old conjugate and a needle obstruction resulted in analytically and even clinically relevant errors in ANA IIF results. All errors were correctly detected by the different defined quality indicators. Traditional Westgard rules, including analytically (and clinically) defined rejection limits were useful in monitoring quality indicators.

CONCLUSIONS

The integration of a total process iQC program in CAD systems, based on the specific FI measurands and performance criteria of the system, adds value to QA.

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.

Performance analysis of automated evaluation of antinuclear antibody indirect immunofluorescent tests in a routine setting

Purpose

Indirect immunofluorescence (IIF) on the human epithelial cell-line HEp-2 (or derivatives) serves as the gold standard in antinuclear antibody (ANA) screening. IIF, and its evaluation, is a labor-intensive method, making ANA testing a major challenge for present clinical laboratories. Nowadays, several automated ANA pattern recognition systems are on the market. In the current study, the EUROPattern Suite is evaluated for its use in daily practice in a routine setting.

Methods

A total of 1033 consecutive routine samples was used to screen for ANA. Results (positive/negative ANA screening, pattern identification and titer) were compared between software-generated results (EUROPattern) and visual interpretation (observer) of automatically acquired digital images.

Results

Considering the visual interpretation as reference, a relative sensitivity of 99.3% and a relative specificity of 88.9% were obtained for negative and positive discrimination by the software (EPa). A good agreement between visual and software-based interpretation was observed with respect to pattern recognition (mean kappa: for 7 patterns: 0.7). Interestingly, EPa software distinguished more patterns per positive sample than the observer (on average 1.5 and 1.2, respectively). Finally, a concordance of 99.3% was observed within the range of 1 titer step difference between EPa and observer.

Conclusions

The ANA IIF results reported by the EPa software are in very good agreement with the results reported by the observer with respect to being negative/positive, pattern recognition and titer, making automated ANA IIF evaluation an objective and time-efficient tool for routine testing.

Electronic supplementary material

The online version of this article (10.1007/s13317-018-0108-y) contains supplementary material, which is available to authorized users.

Automated Processing and Evaluation of Anti-Nuclear Antibody Indirect Immunofluorescence Testing

Indirect immunofluorescence (IIF) is considered by the American College of Rheumatology (ACR) and the international consensus on ANA patterns (ICAP) the gold standard for the screening of anti-nuclear antibodies (ANA). As conventional IIF is labor intensive, time-consuming, subjective, and poorly standardized, there have been ongoing efforts to improve the standardization of reagents and to develop automated platforms for assay incubation, microscopy, and evaluation. In this study, the workflow and performance characteristics of a fully automated ANA IIF system (Sprinter XL, EUROPattern Suite, IFA 40: HEp-20-10 cells) were compared to a manual approach using visual microscopy with a filter device for single-well titration and to technologist reading. The Sprinter/EUROPattern system enabled the processing of large daily workload cohorts in less than 8 h and the reduction of labor hands-on time by more than 4 h. Regarding the discrimination of positive from negative samples, the overall agreement of the EUROPattern software with technologist reading was higher (95.6%) than when compared to the current method (89.4%). Moreover, the software was consistent with technologist reading in 80.6–97.5% of patterns and 71.0–93.8% of titers. In conclusion, the Sprinter/EUROPattern system provides substantial labor savings and good concordance with technologist ANA IIF microscopy, thus increasing standardization, laboratory efficiency, and removing subjectivity.

Diagnostic utility of automated indirect immunofluorescence compared to manual indirect immunofluorescence for anti-nuclear antibodies in patients with systemic rheumatic diseases: A systematic review and meta-analysis

OBJECTIVE

This study aimed to review and compare the analytical and clinical performance of automated indirect immunofluorescence (AIIF) and manual indirect immunofluorescence (MIIF) as anti-nuclear antibody screening assays for patients with systemic rheumatic diseases (SRDs), such as systemic lupus erythematosus (SLE) and systemic sclerosis (SSc).

METHODS

A systematic literature search was performed in the Medline, Embase, Cochrane, Web of Science, and Scopus databases for studies published before August 2017. A bivariate random effects model was used to calculate the summary diagnostic values.

RESULTS

Twenty-two studies involving 6913 positive and 1818 negative samples of MIIF, as well as 524 combined SRD, 132 SLE, and 104 SSc patients, and 520 controls were available for meta-analysis. The summary positive concordance (PC) of qualitative result between AIIF and MIIF was 93.7%, whereas PCs of total pattern (68.5%; homogeneous, 52.3%; speckled, 56.5%; nucleolar, 52.7%; centromere, 51.4%; nuclear dot, 11.7%) and titer (77.8%) exhibited significantly lower values. The summary clinical sensitivities of AIIF vs. MIIF were 84.7% vs 78.2% for combined SRDs, 95.5% vs. 93.9% for SLE, and 86.5% vs. 83.7% for SSc, respectively. Meanwhile, the summary specificities of AIIF vs. MIIF were 75.6% vs. 79.6% for combined SRDs, 74.2% vs. 83.3% for SLE, and 74.2% vs. 83.3% for SSc, respectively. Although the differences in sensitivity and specificity between AIIF and MIIF were not significant in most subgroups, the summary specificity of SLE and SSc showed statistically significant changes.

CONCLUSIONS

Our systematic meta-analysis demonstrates that AIIF is comparable to MIIF in distinguishing between the positive and negative results, and screening SRDs based on clinical sensitivities and standardization. However, improvements in the pattern and titer recognition and clinical specificities are necessary.

New automated indirect immunofluorescent antinuclear antibody testing compares well with established manual immunofluorescent screening and titration for antinuclear antibody on HEp-2 cells

The IIF using the HEp-2 cell substrate should be still considered the "gold standard" techniques for determination of antinuclear antibody (ANA). Standardization and automation can be considered to be still in progress. Aim of this study was to evaluate the performance of the commercially automated indirect immunofluorescent antinuclear HEp-2 antibody assay. The study was designed to compare two commercially available HEp-2 ANA by indirect immunofluorescent antibody assays using a sensitivity panel (120 clinically determined patients) and a specificity panel consisting of 78 clinically confirmed negative patients. We compared the NOVA View^® system [INOVA Diagnostics San Diego, USA] with the new HELIOS Processor from AESKU Systems/AESKU.Diagnostics (Wendelsheim, Germany) to assess their capability for screening, pattern recognition and titration of the samples. These automated methods were directly compared to manual reading of the same processed slides on respective microscopes and also compared with the known clinical information. The results of the two automated methods were in very good agreement with recognizing negative and positive samples. The HELIOS system detected 188 samples correctly as negative or positive versus 187 detected by the NOVA View^® system. The diagnostic sensitivity of the systems was 95.8 versus 96.7 % for HELIOS and NOVA View^®, respectively. The systems exhibited a diagnostic specificity of 93.5 % for the HELIOS system and 91.0 % for the NOVA View^®. Both systems are suitable for fast and reliable detection of positivity/negativity due to their high sensitivity and will lead to a further increase of standardization in autoimmunity.

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.

Design of a Computer-Assisted System to Automatically Detect Cell Types Using ANA IIF Images for the Diagnosis of Autoimmune Diseases

Indirect immunofluorescence technique applied on HEp-2 cell substrates provides the major screening method to detect ANA patterns in the diagnosis of autoimmune diseases. Currently, the ANA patterns are mostly inspected by experienced physicians to identify abnormal cell patterns. The objective of this study is to design a computer-assisted system to automatically detect cell patterns of IIF images for the diagnosis of autoimmune diseases in the clinical setting. The system simulates the functions of modern flow cytometer and provides the diagnostic reports generated by the system to the technicians and physicians through the radar graphs, box-plots, and tables. The experimental results show that, among the IIF images collected from 17 patients, 6 were classified as coarse-speckled, 3 as diffused, 2 as discrete-speckled, 1 as fine-speckled, 2 as nucleolar, and 3 as peripheral patterns, which were consistent with the patterns determined by the physicians. In addition to recognition of cell patterns, the system also provides the function to automatically generate the report for each patient. The time needed for the whole procedure is less than 30 min, which is more efficient than the manual operation of the physician after inspecting the ANA IIF images. Besides, the system can be easily deployed on many desktop and laptop computers. In conclusion, the designed system, containing functions for automatic detection of ANA cell pattern and generation of diagnostic report, is effective and efficient to assist physicians to diagnose patients with autoimmune diseases. The limitations of the current developed system include (1) only a unique cell pattern was considered for the IIF images collected from a patient, and (2) the cells during the process of mitosis were not adopted for cell classification.

EUROPattern Suite technology for computer-aided immunofluorescence microscopy in autoantibody diagnostics

Antinuclear autoantibodies (ANA) are highly informative biomarkers in autoimmune diagnostics. The increasing demand for effective test systems, however, has led to the development of a confusingly large variety of different platforms. One of them, the indirect immunofluorescence (IIF), is regarded as the common gold standard for ANA screening, as described in a position statement by the American College of Rheumatology in 2009. Technological solutions have been developed aimed at standardization and automation of IIF to overcome methodological limitations and subjective bias in IIF interpretation. In this review, we present the EUROPattern Suite, a system for computer-aided immunofluorescence microscopy (CAIFM) including automated acquisition of digital images and evaluation of IIF results. The system was originally designed for ANA diagnostics on human epithelial cells, but its applications have been extended with the latest system update version 1.5 to the analysis of antineutrophil cytoplasmic antibodies (ANCA) and anti-dsDNA antibodies.

HEp-2 Cell Classification Using Descriptors Fused into the Dissimilarity Space into the Dissimilarity Space

Autoimmune diseases are strictly connected with the presence of autoantibodies in patient serum. Detection of Antinucleolar Antibodies (ANAs) in patient serum is performed using a laboratory technique named Indirect Immunofluorescence (IIF) followed by manual evaluation on the acquired slides from specialized personnel. In this procedure, several limitations appear and several automatic techniques have been proposed for the task of ANA detection. In this work we present a method achieving state-of-the-art performance on a publicly available dataset. More precisely, two powerful and rotation invariant descriptors are incorporated into a two stage classification scheme where the feature vectors are represented and fused in the dissimilarity space. Then, in a second level dissimilarity vectors are classified using a linear SVM classifier. Evaluation on the HEp-2 cell contest dataset yields a 70.16% performance on cell-level classification. Furthermore we provide results in Image Level Classification where a 78.57% classification rate was achieved.

The classification of Crithidia luciliae immunofluorescence test (CLIFT) using a novel automated system

Introduction

In recent years, there has been an increased demand for computer-aided diagnosis (CAD) tools to support clinicians in the field of indirect immunofluorescence. To this aim, academic and industrial research is focusing on detecting antinuclear, anti-neutrophil, and anti-double-stranded (anti-dsDNA) antibodies. Within this framework, we present a CAD system for automatic analysis of dsDNA antibody images using a multi-step classification approach. The final classification of a well is based on the classification of all its images, and each image is classified on the basis of the labeling of its cells.

Methods

We populated a database of 342 images—74 positive (21.6%) and 268 negative (78.4%)— belonging to 63 consecutive sera: 15 positive (23.8%) and 48 negative (76.2%). We assessed system performance by using k-fold cross-validation. Furthermore, we successfully validated the recognition system on 83 consecutive sera, collected by using different equipment in a referral center, counting 279 images: 92 positive (33.0%) and 187 negative (67.0%).

Results

With respect to well classification, the system correctly classified 98.4% of wells (62 out of 63). Integrating information from multiple images of the same wells recovers the possible misclassifications that occurred at the previous steps (cell and image classification). This system, validated in a clinical routine fashion, provides recognition accuracy equal to 100%.

Conclusion

The data obtained show that automation is a viable alternative for Crithidia luciliae immunofluorescence test analysis.

Molecular diagnosis in autoimmune skin blistering conditions

Blister formation in skin and mucous membranes results from a loss of cell-cell or cell-matrix adhesion and is a common outcome of pathological events in a variety of conditions, including autoimmune and genetic diseases, viral and bacterial infections, or injury by physical and chemical factors. Autoantibodies against structural components maintaining cell-cell and cell-matrix adhesion induce tissue damage in autoimmune blistering diseases. Detection of these autoantibodies either tissue-bound or circulating in serum is essential to diagnose the autoimmune nature of disease. Various immunofluorescence methods as well as molecular immunoassays, including enzyme-linked immunosorbent assay and immunoblotting, belong to the modern diagnostic algorithms for these disorders. There is still a considerable need to increase awareness of the rare autoimmune blistering diseases, which often show a severe, chronic-relapsing course, among physicians and the public. This review article describes the immunopathological features of autoimmune bullous diseases and the molecular immunoassays currently available for their diagnosis and monitoring.

Automated antinuclear immunofluorescence antibody screening: a comparative study of six computer-aided diagnostic systems

BACKGROUND

Indirect immunofluorescence (IIF) plays an important role in immunological assays for detecting and measuring autoantibodies. However, the method is burdened by some unfavorable features: the need for expert morphologists, the subjectivity of interpretation, and a low degree of standardization and automation. Following the recent statement by the American College of Rheumatology that the IIF technique should be considered as the standard screening method for the detection of anti-nuclear antibodies (ANA), the biomedical industry has developed technological solutions which might significantly improve automation of the procedure, not only in the preparation of substrates and slides, but also in microscope reading.

METHODS

We collected 104 ANA-positive sera from patients with a confirmed clinical diagnosis of autoimmune disease and 40 ANA-negative sera from healthy blood donors. One aliquot of each serum, without information about pattern and titer, was sent to six laboratories of our group, where the sera were tested with the IIF manual method provided by each of the six manufacturers of automatic systems. Assignment of result (pos/neg), of pattern and titer was made by consensus at a meeting attended by all members of the research team. Result was assigned if consensus for pos/neg was reached by at least four of six certifiers, while for the pattern and for the titer, the value observed with higher frequency (mode) was adopted. Seventeen ANA-positive sera and six ANA-negative sera were excluded. Therefore, the study with the following automatic instrumentation was conducted on 92 ANA-positive sera and on 34 ANA-negative sera: Aklides, EUROPattern, G-Sight (I-Sight-IFA), Helios, Image Navigator, and Nova View. Analytical imprecision was measured in five aliquots of the same serum, randomly added to the sample series.

RESULTS

Overall sensitivity of the six automated systems was 96.7% and overall specificity was 89.2%. Most false negatives were recorded for cytoplasmic patterns, whereas among nuclear patterns those with a low level of fluorescence (i.e., multiple nuclear dots, midbody, nuclear rim) were sometimes missed. The intensity values of the light signal of various instruments showed a good correlation with the titer obtained by manual reading (Spearman's rho between 0.672 and 0.839; P<0.0001 for all the systems). Imprecision ranged from 1.99% to 25.2% and, for all the systems, it was lower than that obtained by the manual IIF test (39.1%). The accuracy of pattern recognition, which is for now restricted to the most typical patterns (homogeneous, speckled, nucleolar, centromere, multiple nuclear dots and cytoplasmic) was limited, ranging from 52% to 79%.

CONCLUSIONS

This study, which is the first to compare the diagnostic accuracy of six systems for automated ANA-IIF reading on the same series of sera, showed that all systems are able to perform very well the task for which they were created. Indeed, cumulative automatic discrimination between positive and negative samples had 95% accuracy. All the manufacturers are actively continuing the development of new and more sophisticated software for a better definition in automatic recognition of patterns and light signal conversion in end-point titer. In the future, this may avert the need for serum dilution for titration, which will be a great advantage in economic terms and time-saving.

Benchmarking HEp-2 cells classification methods

In this paper, we report on the first edition of the HEp-2 Cells Classification contest, held at the 2012 edition of the International Conference on Pattern Recognition, and focused on indirect immunofluorescence (IIF) image analysis. The IIF methodology is used to detect autoimmune diseases by searching for antibodies in the patient serum but, unfortunately, it is still a subjective method that depends too heavily on the experience and expertise of the physician. This has been the motivation behind the recent initial developments of computer aided diagnosis systems in this field. The contest aimed to bring together researchers interested in the performance evaluation of algorithms for IIF image analysis: 28 different recognition systems able to automatically recognize the staining pattern of cells within IIF images were tested on the same undisclosed dataset. In particular, the dataset takes into account the six staining patterns that occur most frequently in the daily diagnostic practice: centromere, nucleolar, homogeneous, fine speckled, coarse speckled, and cytoplasmic. In the paper, we briefly describe all the submitted methods, analyze the obtained results, and discuss the design choices conditioning the performance of each method.

A decision support system for Crithidia luciliae image classification

OBJECTIVE

Systemic lupus erythematosus is a connective tissue disease affecting multiple organ systems and characterised by a chronic inflammatory process. It is considered a very serious sickness, further to be classified as an invalidating chronic disease. The recommended method for its detection is the indirect immunofluorescence (IIF) based on Crithidia Luciliae (CL) substrate. Hoverer, IIF is affected by several issues limiting tests reliability and reproducibility. Hence, an evident medical demand is the development of computer-aided diagnosis tools that can offer a support to physician decision.

METHODS

In this paper we propose a system that classifies CL wells integrating information extracted from different images. It is based on three main decision phases. Two steps, named as threshold-based classification and single cells recognition, are applied for image classification. They minimise false negative and false positive classifications, respectively. Feature extraction and selection have been carried out to determine a compact set of descriptors to distinguish between positive and negative cells. The third step applies majority voting rule at well recognition level, enabling us to recover possible errors provided by previous phases.

RESULTS

The system performance have been evaluated on an annotated database of IIF CL wells, composed of 63 wells for a total of 342 images and 1487 cells. Accuracy, sensitivity and specificity of image recognition step are 99.4%, 98.6% and 99.6%, respectively. At level of well recognition, accuracy, sensitivity and specificity are 98.4%, 93.3% and 100.0%, respectively. The system has been also validated in a daily routine fashion on 48 consecutive analyses of hospital outpatients and inpatients. The results show very good performance for well recognition (100% of accuracy, sensitivity and specificity), due to the integration of cells and images information.

CONCLUSIONS

The described recognition system can be applied in daily routine in order to improve the reliability, standardisation and reproducibility of CL readings in IIF.

Autoantibody detection using indirect immunofluorescence on HEp-2 cells

The detection of autoantibodies is an important element in the diagnosis and monitoring of disease progression in patients with autoimmune diseases. In laboratory diagnostic tests for connective tissue and autoimmune liver diseases, indirect immunofluorescence on HEp-2 cells plays a central role in a multistage diagnostic process. Despite the high quality of diagnostics, findings at different laboratories can differ considerably due to a lack of standardization, as well as subjective factors. The present paper formulates recommendations for the standardized processing and interpretation of the HEp-2 cell test for the detection of non-organ-specific (especially antinuclear) antibodies. It provides requirements regarding the diagnostic tests used, instructions for laboratory procedure and evaluation, and recommendations for interpretation. For an optimal laboratory diagnostic process, it is useful to have an informative, tentative clinical diagnosis and an experienced laboratory diagnostician. In addition, the following key elements are recommended: initial screening using indirect immunofluorescence on carefully chosen HEp-2 cells beginning with a serum dilution of 1:80 and evaluation under a microscope with powerful illumination; results from a titer of 1:160 upwards being considered positive; internal laboratory quality control; and standardized interpretation. The aim is to improve diagnostic tests and care of patients with autoimmune diseases as a central concern of the European Autoimmunity Standardization Initiative (EASI).

Indirect immunofluorescence in autoimmune diseases: Assessment of digital images for diagnostic purpose

BACKGROUND

The recommended method for antinuclear antibodies (ANA) detection is indirect immunofluorescence (IIF). To pursue a high image quality without artefacts and reduce interobserver variability, this study aims at evaluating the reliability of automatically acquired digital images of IIF slides for diagnostic purposes.

METHODS

Ninety-six sera were screened for ANA by IIF on HEp-2 cells. Two expert physicians looking at both the fluorescence microscope and the digital images on computer monitor performed a blind study to evaluate fluorescence intensity and staining pattern. Cohen's kappa was used as an agreement evaluator between methods and experts.

RESULTS

Considering fluorescence intensity, there is a substantial agreement between microscope and monitor analysis in both physicians. Agreement between physicians was substantial at the microscope and perfect at the monitor. Considering IIF pattern, there was a substantial and moderate agreement between microscope and monitor analysis in both physicians. Kappa between physicians was substantial both at the microscope and at the monitor.

CONCLUSIONS

These preliminary results suggest that digital media is a reliable tool to help physicians in detecting autoantibodies in IIF. Our data represent a first step to validate the use of digital images, thus offering an opportunity for standardizing and automatizing the detection of ANA by IIF.

Present and future of the autoimmunity laboratory

At present, autoimmunity laboratories are very dynamic owing to the constant and increasing availability of new tests, mainly due to the detection of new autoantibodies. The main characteristic of the autoimmunity laboratory and the one that differentiates it from other laboratories that use immunoassays as basic techniques is that it determines antibodies (autoantibodies) and not antigens. For this reason, immunoassay techniques must employ antigens as reagents. Indirect immunofluorescence has and continues to be a basic technique in autoimmunity studies. However, over the last few years, a significant trend at autoimmunity laboratories has been the gradual replacement of immunofluorescence microscopy by immunoassay. Of the several different forms of immunoassay, the enzyme-linked immunosorbent assay (ELISA) format is the one most used in autoimmunity laboratories. Recombinant DNA technology has allowed the production of large quantities of antigens for autoantibody analysis. Flow cytometry for the analysis of microsphere-based immunoassays allows the simultaneous measurement of several autoantibodies. Likewise, autoantigen microarrays provide a practical means to analyse biological fluids in the search for a high number of autoantibodies. We are now at the beginning of an era of multiplexed analysis, with a high capacity of autoantibody specificities. Future trends in this field include immunoassays with greater analytical sensitivity, simultaneous multiplexed capability, the use of protein microarrays, and the use of other technologies such as microfluidics.

Establishment of HEp-2 cell preparation for automated analysis of ANA fluorescence pattern

BACKGROUND

Identification of antinuclear antibodies (ANAs) has large clinical importance for the assessment of autoimmune diseases. HEp-2 cell preparations on microscopic slides are commonly used as antigenic substrate. Methods used for cell preparation are important for ANA pattern analysis; however, these methods differ widely and are mostly not specified.

METHODS

HEp-2 cells were fixed using acetic acid-ethanol, methanol-acetone, acetone, formaldehyde, paraformaldehyde, or glutaraldehyde. Morphological analysis was done after haematoxylin-eosin staining and DAPI-staining of cell nuclei.

RESULTS

The results demonstrate a high variability of cell and nuclear morphology depending on the used fixatives. Aldehyde fixatives conserved the cell structures best, acetone fixatives revealed remarkable changes.

CONCLUSIONS

After selecting appropriate fixation procedures to preserve nuclear structures further experiments are necessary to find out which fixation procedure preserves the disease-linked antigens the best way and are, therefore, suitable to be used in ANA-testing of AABs.

Novel aspects of systems biology and clinical cytomics

The area of Cytomics and Systems Biology became of great impact during the last years. In some fields of the leading cytometric techniques it represents the cutting edge today. Many different applications/variations of multicolor staining were developed for flow- or slide-based cytometric analysis of suspensions and sections to whole animal analysis. Multispectral optical imaging can be used for studying immunological and tumorigenic processes. New methods resulted in the establishment of lipidomics as the systemic research of lipids and their behavior. All of these development push the systemic approach of the analysis of biological specimens to enhance the outcome in the clinic and in drug discovery programs.

Slide-based cytometry and predictive medicine: The 8th Leipziger workshop and the 1st international workshop on slide-based cytometry

Slide-based cytometry (SBC) and related techniques offer unique tools to perform complex diagnostic procedures at very early disease stages. Multicolor or polychromatic analysis of cells by SBC is of special importance, not only as a cytomics technology platform, but for patients with low blood volume such as neonates. The exact knowledge of the location of each cell on the slide allows the specimen to be restained and subsequently reanalyzed. These separate measurements can be fused to one data file (merging), increasing the information obtained per cell. Relocalization and optical evaluation of the cells, a typical feature of SBC, can be of integral importance for cytometric analysis. Predictive medicine is aimed at the detection of changes in the patient's state prior to the manifestation of deterioration or improvement. Such instances are concerned with multiorgan failure in sepsis or noninfectious posttraumatic shock in intensive care patients, or the pretherapeutic identification of high risk patients in cancer cytostatic therapy. Early anti-infectious or anti-shock therapy, as well as curative chemotherapy in combination with stem cell transplantation, may provide better survival chances for the patient as well as concomitant cost containment. Predictive medicine-guided, individualized, early reduction or cessation of therapy may lower or abrogate potential therapeutic side effects (individualized medicine). With the 8th Leipziger Workshop and the 1st International Workshop on Slide-Based Cytometry, cytomics technologies moved to more practical applications in the clinics and the clinical laboratory. This development will be continued in 2004, at the upcoming Leipziger Workshop and the International Workshop on Slide-Based Cytometry.