Computer Aided Diagnosis of Systemic Lupus Erythematosus Through Antinuclear Antibody Image Pattern Recognition

This study focuses on diagnosis of systemic lupus erythematosus through pattern recognition of antinuclear Antibodies. SLE is an autoimmune disease. In the human body the immune system attacks its own healthy cells, this results in widespread inflammation and tissue damage and affects the organs like skin, brain, lungs, kidneys, and blood vessels. ANA test (screening test) detects antinuclear antibodies in your blood. After this ANA screening test, Investigation called the anti-dsDNA (anti-double stranded DNA) test is conducted for confirmation of SLE. This study is to replace this anti-dsDNA confirmatory test with pattern recognition of anti-nuclear antibodies which is more economic and time savings. Majorly in three patterns SLE probability is high. They are: homogenous pattern, cytoplasmic pattern, speckled pattern. From the image of the ANA screening test, the single cell pattern image is cropped and a watershed algorithm is used to get segmentation of the nucleus, followed by extracting of shape features from the segmented image. With these shape features we can classify the patterns of ANA cells. This pattern recognition is used for pre-screening. Further, along with appropriate patterns of SLE, clinical biomarkers, blood biomarkers, and organ specific biomarkers are also considered and get accurate diagnosis of SLE

Authors

Institutions

Publication Details

Published
2025-04-23
DOI
https://doi.org/10.1109/raeeucci63961.2025.11048341
Primary Topic
Systemic Lupus Erythematosus Research
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Computer Aided Diagnosis of Systemic Lupus Erythematosus Through Antinuclear Antibody Image Pattern Recognition

Leela Kakithakara Vajravelu, M Sameera Fathimal, S. P. Angeline Kirubha, Teja Tallam et al.
1 links
Systemic Lupus Erythematosus Research
article

Computer Aided Diagnosis of Systemic Lupus Erythematosus Through Antinuclear Antibody Image Pattern Recognition

Leela Kakithakara Vajravelu, M Sameera Fathimal, S. P. Angeline Kirubha, Teja Tallam, A Jeya Prabha, T Jayapraksh
article en

Abstract

This study focuses on diagnosis of systemic lupus erythematosus through pattern recognition of antinuclear Antibodies. SLE is an autoimmune disease. In the human body the immune system attacks its own healthy cells, this results in widespread inflammation and tissue damage and affects the organs like skin, brain, lungs, kidneys, and blood vessels. ANA test (screening test) detects antinuclear antibodies in your blood. After this ANA screening test, Investigation called the anti-dsDNA (anti-double stranded DNA) test is conducted for confirmation of SLE. This study is to replace this anti-dsDNA confirmatory test with pattern recognition of anti-nuclear antibodies which is more economic and time savings. Majorly in three patterns SLE probability is high. They are: homogenous pattern, cytoplasmic pattern, speckled pattern. From the image of the ANA screening test, the single cell pattern image is cropped and a watershed algorithm is used to get segmentation of the nucleus, followed by extracting of shape features from the segmented image. With these shape features we can classify the patterns of ANA cells. This pattern recognition is used for pre-screening. Further, along with appropriate patterns of SLE, clinical biomarkers, blood biomarkers, and organ specific biomarkers are also considered and get accurate diagnosis of SLE

SRM Institute of Science and Technology (IN), Trichy SRM Medical College Hospital and Research Centre (IN), AAA College of Engineering and Technology, Karpagam Academy of Higher Education (IN)
Good health and well-being
Openalex Percentile: Top 75%
Systemic Lupus Erythematosus Research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.