Application of Supervised Machine Learning to Recognize Competent Level and Mixed Antinuclear Antibody Patterns Based on ICAP International Consensus

Wu, Yi; Sheu, Ruey-Kai; Chung, Chih Wei; Wu, Yixuan; Ou, Chiao Chi; Hsiao, Chien Wen; Chang, Huang Chen; Huang, Ying; Lo, Win Tsung; Chen, Lun-Chi; Huang, Chien Chung; Hsieh, Tsu‐Yi; Huang, Wen Nan; Yen, Tsai Hung; Chen, Yun Wen; Chen, Chia Yu

doi:10.3390/diagnostics11040642

Cited by 8 publications

(46 citation statements)

References 38 publications

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“…In addition, six different deep learning methods, including InceptionResNetV2, MobileNetV2, Xception, VGG19, ResNet50V2, and DenseNet121 are implemented and compared in the classification of IIF images in [ 14 ]. With pretrained models, InceptionResnetV2 achieves the highest F 1 score of 0.86, so InceptionResnetV2 is chosen as the deep learning method in this paper.…”

Section: Related Work and Backgroundmentioning

confidence: 99%

“…Traditional machine learning classification methods (such as SVM) [ 7 ] k-nearest neighbours algorithm (KNN) [ 8 ], random forest (RF) [ 9 ], and the ensemble classifier (ECLF) [ 10 ] utilize various image features including scale-invariant feature transform (SIFT) [ 11 ], local binary pattern (LBP) [ 12 ], co-ccurrence among adjacent LBPs (CoALBP) [ 13 ], and rotation invariant cooccurrence among adjacent LBPs (RIC-LBP) [ 13 ] are compared to find the optimal combination of the learning method and features. Finally, the deep learning method InceptionResNetV2 is used for classification due to the highest accuracy shown in [ 14 ]. The transfer learning with the pretrained model is also used to improve the performance.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Segmentation and Classification for Antinuclear Antibody Images Based on Deep Learning

Xie

Chen

et al. 2023

Computational Intelligence and Neuroscience

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Antinuclear antibodies (ANAs) testing is the main serological diagnosis screening test for autoimmune diseases. ANAs testing is conducted principally by the indirect immunofluorescence (IIF) on human epithelial cell-substrate (HEp-2) protocol. However, due to its high variability and human subjectivity, there is an insistent need to develop an efficient method for automatic image segmentation and classification. This article develops an automatic segmentation and classification framework based on artificial intelligence (AI) on the ANA images. The Otsu thresholding method and watershed segmentation algorithm are adopted to segment IIF images of cells. Moreover, multiple texture features such as scale-invariant feature transform (SIFT), local binary pattern (LBP), cooccurrence among adjacent LBPs (CoALBP), and rotation invariant cooccurrence among adjacent LBPs (RIC-LBP) are utilized. Firstly, this article adopts traditional machine learning methods such as support vector machine (SVM), k-nearest neighbor algorithm (KNN), and random forest (RF) and then uses ensemble classifier (ECLF) combined with soft voting rules to merge these machine learning methods for classification. The deep learning method InceptionResNetV2 is also utilized to train on the classification of cell images. Eventually, the best accuracy of 0.9269 on the Changsha dataset and 0.9635 on the ICPR 2016 dataset for the traditional methods is obtained by a combination of SIFT and RIC-LBP with the ECLF classifier, and the best accuracy obtained by the InceptionResNetV2 is 0.9465 and 0.9836 separately, which outperforms other schemes.

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Section: Related Work and Backgroundmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic Segmentation and Classification for Antinuclear Antibody Images Based on Deep Learning

Xie

Chen

et al. 2023

Computational Intelligence and Neuroscience

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“…Identifying GCA features from temporal artery biopsy reports (13) Text Transformer Accurate auditing of temporal artery biopsy reports can be performed using deep learning; however, this performance dropped when tested across centers. Classifying HEp-2 cells based on ANA IIF patterns (29) Images CNN Automated ANA classification based on HEp-2 cells is approaching expert human performance. OESS from synovial ultrasound (44) Images CNN Deep learning can identify synovitis on ultrasound with a high degree of accuracy.…”

Section: Learning From Textmentioning

confidence: 99%

“…State‐of‐the‐art models using either technique have achieved accuracies exceeding 97% ( 26 , 27 ), which favorably compare to human accuracy (73.3%) ( 28 ). However, this comparison has been criticized, as the task of classifying a single HEp‐2 cell, isolated from the broader context of the specimen, is not representative of how IIF tests for ANAs are performed in real clinical practice ( 29 ). Moreover, these methods are developed, tested, and validated using limited data sets ( 30 , 31 , 32 , 33 ).…”

Section: Learning From Ehrsmentioning

confidence: 99%

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Artificial Intelligence and Deep Learning for Rheumatologists

McMaster

Bird

Liew³

et al. 2022

Arthritis & Rheumatology

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Deep learning has emerged as the leading method in machine learning, spawning a rapidly growing field of academic research and commercial applications across medicine. Deep learning could have particular relevance to rheumatology if correctly utilized. The greatest benefits of deep learning methods are seen with unstructured data frequently found in rheumatology, such as images and text, where traditional machine learning methods have struggled to unlock the trove of information held within these data formats. The basis for this success comes from the ability of deep learning to learn the structure of the underlying data. It is no surprise that the first areas of medicine that have started to experience impact from deep learning heavily rely on interpreting visual data, such as triaging radiology workflows and computer-assisted colonoscopy. Applications in rheumatology are beginning to emerge, with recent successes in areas as diverse as detecting joint erosions on plain radiography, predicting future rheumatoid arthritis disease activity, and identifying halo sign on temporal artery ultrasound. Given the important role deep learning methods are likely to play in the future of rheumatology, it is imperative that rheumatologists understand the methods and assumptions that underlie the deep learning algorithms in widespread use today, their limitations and the landscape of deep learning research that will inform algorithm development, and clinical decision support tools of the future. The best applications of deep learning in rheumatology must be informed by the clinical experience of rheumatologists, so that algorithms can be developed to tackle the most relevant clinical problems.

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Recognition of rare antinuclear antibody patterns based on a novel attention-based enhancement framework

Zeng,

Gao,

Gao

et al. 2024

Briefings in Bioinformatics

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Rare antinuclear antibody (ANA) pattern recognition has been a widely applied technology for routine ANA screening in clinical laboratories. In recent years, the application of deep learning methods in recognizing ANA patterns has witnessed remarkable advancements. However, the majority of studies in this field have primarily focused on the classification of the most common ANA patterns, while another subset has concentrated on the detection of mitotic metaphase cells. To date, no prior research has been specifically dedicated to the identification of rare ANA patterns. In the present paper, we introduce a novel attention-based enhancement framework, which was designed for the recognition of rare ANA patterns in ANA-indirect immunofluorescence images. More specifically, we selected the algorithm with the best performance as our target detection network by conducting comparative experiments. We then further developed and enhanced the chosen algorithm through a series of optimizations. Then, attention mechanism was introduced to facilitate neural networks in expediting the learning process, extracting more essential and distinctive features for the target features that belong to the specific patterns. The proposed approach has helped to obtained high precision rate of 86.40%, 82.75% recall, 84.24% F1 score and 84.64% mean average precision for a 9-category rare ANA pattern detection task on our dataset. Finally, we evaluated the potential of the model as medical technologist assistant and observed that the technologist’s performance improved after referring to the results of the model prediction. These promising results highlighted its potential as an efficient and reliable tool to assist medical technologists in their clinical practice.

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Application of Supervised Machine Learning to Recognize Competent Level and Mixed Antinuclear Antibody Patterns Based on ICAP International Consensus

Cited by 8 publications

References 38 publications

Automatic Segmentation and Classification for Antinuclear Antibody Images Based on Deep Learning

Automatic Segmentation and Classification for Antinuclear Antibody Images Based on Deep Learning

Artificial Intelligence and Deep Learning for Rheumatologists

Recognition of rare antinuclear antibody patterns based on a novel attention-based enhancement framework

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