ANA IIF Methods: Complimentary Rather Than Competitive

The introduction of Automated Indirect Immunofluorescence (ANA IIF) systems has significantly improved the efficiency of autoimmune diagnostics by streamlining image acquisition, pattern recognition and titer reporting. These technologies help laboratories manage increasing workloads while enhancing standardization and reducing inter-observer variability. Through consistent processing and digital archiving capabilities, automated systems contribute to greater reproducibility and quality assurance in ANA testing workflows.

Despite these advantages, automation is not designed to replace the expertise of skilled laboratory professionals. Manual microscopy remains essential for evaluating complex fluorescence patterns, atypical staining characteristics, and challenging clinical cases that may not be accurately classified by software algorithms alone. Experienced microscopists provide critical judgment and contextual interpretation, ensuring that subtle findings are recognized and correctly correlated with patient information.

The most effective approach combines the strengths of both methodologies within an integrated diagnostic workflow. Automated ANA IIF systems can efficiently handle routine screening and standard cases, while manual microscopy serves as a valuable confirmatory and problem-solving tool for difficult interpretations. Therefore, rather than competing with one another, the two methods work synergistically to enhance diagnostic performance.

References

1. Snježana Kos, Annelies Berden, Elisabeth Zirkzee, Technical Developments in ANA Diagnostics: Comparison of a Manual Method to the Visual Readout of Digital Images and Software Analysis, The Journal of Applied Laboratory Medicine 2022;7(1):379–382.

2. Loock CD, Egerer K, Feist E, Burmester G. Automated evaluation of ANA under real-life conditions. RMD Open. 2017;3:e000409.

3. Ricchiuti V, Adams J, Hardy DJ, Katayev A, Fleming JK. Automated Processing and Evaluation of Anti-Nuclear Antibody Indirect Immunofluorescence Testing. Front Immunol. 2018;9:927.

4. Meroni PL, Bizzaro N, Cavazzana I, Borghi MO, Tincani A. Automated tests of ANA immunofluorescence as throughput autoantibody detection technology: strengths and limitations. BMC Med. 2014;12:38.

5. Yoo IY, Oh JW, Cha HS, Koh EM, Kang ES. Performance of an Automated Fluorescence Antinuclear Antibody Image Analyzer. Ann Lab Med. 2017;37(3):240-247.

6. Van den Bremt S, Schouwers S, Van Blerk M, Van Hoovels L. ANA IIF Automation: Moving towards Harmonization? Results of a Multicenter Study. J Immunol Res. 2017;2017:6038137.

7. Choi H-W, Kwon YJ, Park J-H, Lee S-Y, Chun S, Won EJ, Lee JH, Choi H-J, Kim SH, Shin M-G, Shin J-H and Kee S-J.Evaluation of a Fully Automated Antinuclear Antibody Indirect Immunofluorescence Assay in Routine Use. Front. Immunol. 2020;11:607541.

8. Ricchiuti V, Adams J, Hardy DJ, Katayev A and Fleming JK. Automated Processing and Evaluation of Anti-Nuclear Antibody Indirect Immunofluorescence Testing. Front. Immunol.2018; 9:927.

9. Marta Francisca Corrà, Mafalda Sousa, Inês Reis, Fabiana Tanganelli, Nuno Vila-Chã, Ana Paula Sousa, Rui Magalhães, Paula Sampaio, Ricardo Taipa, Luís Maia, Advantages of an Automated Method Compared With Manual Methods for the Quantification of Intraepidermal Nerve Fiber in Skin Biopsy, Journal of Neuropathology & Experimental Neurology 2021;80(7):685–694.