Overview of Fingerprint-Based Blood-Grouping using Various Tools and Techniques
DOI:
https://doi.org/10.37506/65z8av60Keywords:
Biometric Blood Typing, Deep Learning and Machine Learning, AI-based Pattern Recognition, Predictive Healthcare Technology, Genetic-Biometric CorrelationAbstract
Blood grouping plays a crucial role in medical diagnostics, transfusion practices, and forensic investigations. Traditional
methods for determining blood groups involve serological testing of blood samples, which, while accurate, require
invasive procedures, trained personnel, and time. Researchers have recently explored non-invasive alternatives, among
which biometric approaches such as fingerprint analysis have gained attention. The emerging field of fingerprint-based
blood grouping presents a revolutionary, non-invasive alternative by leveraging the unique biochemical composition of
fingerprint residues and the distinct patterns of epidermal ridges. This approach offers significant value by enabling rapid,
cost-effective, and hygienic blood type determination, with profound benefits for enhancing emergency response efficiency,
streamlining donor registration in blood banks, and advancing forensic investigations. The purpose of this comprehensive
review is to synthesize current research and provide a detailed overview of the methodologies underpinning this
technology. We examine the scientific basis for the correlation between fingerprint patterns and ABO/Rh blood groups,
evaluate traditional chemical and spectroscopic techniques, and analyze the cutting-edge integration of artificial intelligence
(AI) and deep learning models for automated classification. Furthermore, the review discusses the practical applications,
advantages, and current limitations of the technology, concluding with an outlook on future directions for research and
implementation. This paper underscores the transformative potential of fingerprint-based blood grouping as a viable and
promising solution for modern medical and forensic challenges. However, further research with larger and more genetically
diverse populations is required to validate and refine the predictive models. In conclusion, while fingerprint-based blood
grouping cannot yet replace conventional methods, it offers a compelling supplementary tool for rapid, non-invasive blood
group estimation.
References
1. Isbt. A deep learning approach to the genetic
prediction of blood group antigens [Internet]. The
International Society of Blood Transfusion (ISBT).
Available from: https://www.isbtweb.org/resource/
a-deep-learning-approach-to-the-genetic-predictionof-
blood-group-antigens.html
2. Chaudhary S, Deuja S, Alam M, Karmacharya P,
Mondal M. Fingerprints as an Alternative Method to
Determine ABO and Rh Blood Groups. JNMA J Nepal
Med Assoc. 2017 Oct-Dec;56(208):426-31. PMID:
29453474.
3. Ostrovsky D, Ostrovsky M. Mechanism of DNA
chemical denaturation. ChemRxiv [Preprint]. 2025
Mar 9. doi:10.26434/chemrxiv-2024-gstxb-v4.
4. Volume 13 Issue 2 [Internet]. Available from:
https://www.medpulse.in/Forensic%20Medicine/
html_13_2_2.php
5. Fayrouz INE, Farida N, Irshad AH. Relation between
fingerprints and different blood groups. Journal
of Forensic and Legal Medicine [Internet]. 2011
Oct 22;19(1):18–21. Available from: https://doi.
org/10.1016/j.jflm.2011.09.004
6. Kc S, Maharjan N, Adhikari N, Shrestha P.
Qualitative analysis of primary fingerprint pattern
in different blood group and gender in Nepalese.
Anatomy Research International [Internet]. 2018
Jan 18; 2018:1–7. Available from: https://doi.
org/10.1155/2018/2848974
7. M.S.T. Gowda et al. A study to evaluate the relationship
between dermatoglyphic features and blood groups J
Anat Soc Ind (1996)
8. Patil V, Ingle DR. An association between fingerprint
patterns with blood group and lifestyle-based
diseases: a review. Artificial Intelligence Review
[Internet]. 2020 Aug 18;54(3):1803–39. Available from:
https://doi.org/10.1007/s10462-020-09891-w
9. Bharadwaja A, Saraswat PK, Agrawal SK, Banerji P,
Bharadwaja S. Pattern of finger-prints in different
ABO blood groups. J Indian Acad Forensic Med.
2004;21:1-4. doi:10.1177/0971097320040103.
10. Araújo DC, Veloso AA, De Oliveira Filho RS, Giraud
MN, Raniero LJ, Ferreira LM, et al. Finding reduced
Raman spectroscopy fingerprint of skin samples
for melanoma diagnosis through machine learning.
Artificial Intelligence in Medicine [Internet]. 2021
Aug 29; 120:102161. Available from: https://doi.
org/10.1016/j.artmed.2021.102161
11. Manikandan S, Devisha Mani L, Vijayakumar
S, Palanisamy GS, Ponnusamy P, Jayakar SLL.
Dermatoglyphics and their relationship with blood
group: An exploration. Journal of Pharmacy and
Bio allied Sciences [Internet]. 2019 Jan 1;11(6):285.
Available from: https://doi.org/10.4103/jpbs.
jpbs_13_19
12. View of A Novel Approach for ABO Blood Group
Prediction using Fingerprint through Optimized
Convolutional Neural Network [Internet]. Available
from: https://ijisae.org/index.php/IJISAE/article/
view/1582/683
13. Adgaonkar A, Vinoth R, Raghuveer K, Misra S, Prasad
P, Ramamurthy M. AI-driven classification and
SSRN Electron J. 2025. doi:10.2139/ssrn.5085490.
14. Zhu Y, Yin X, Hu J. Finger GAN: a Constrained
Fingerprint generation scheme for latent fingerprint
enhancement. IEEE Transactions on Pattern Analysis
and Machine Intelligence [Internet]. 2023 Jan
1;1–14. Available from: https://doi.org/10.1109/
tpami.2023.3236876
15. Rastogi A, Bashar MdA, Sheikh NA. Relation of
primary fingerprint patterns with gender and blood
group: a dermatoglyphic study from a tertiary care
institute in eastern India. Cureus [Internet]. 2023
May 2; Available from: https://doi.org/10.7759/
cureus.38459
16. Deshpande UU, Male math VS. A study on Automatic
Latent Fingerprint identification system. Journal
of Computer Science Research [Internet]. 2022
Feb 28;4(1):38–50. Available from: https://doi.
org/10.30564/jcsr.v4i1.4388
17. Altayar M, Alqaraleh M, Alzboon M, Almagharbeh W.
Revolutionizing blood banks: AI-driven fingerprint–
blood group correlation for enhanced safety. Data
Metadata. 2025;4:894. doi:10.56294/dm2025894.
18. Deopa D, Prakash C, Tayal I. A study of fingerprint
in relation to gender and blood group among medical
students in Uttarakhand region. J Indian Acad Forensic
Med. 2014;36:23–7. doi:10.1177/0971097320140106.
19. Ijraset. (n.d.). Blood group detection and management using
advanced deep learning and fingerprint imaging methods.
IJRASET. https://www.ijraset.com/research-paper/
blood-group-detection-and-management-usingadvanced-
deep-learning-and-fingerprint-imagingmethods
20. Moslemi, C., Sækmose, S., Larsen, R., Brodersen, T.,
Bay, J. T., Didriksen, M., Nielsen, K. R., Bruun, M. T.,
Dowsett, J., Dinh, K. M., Mikkelsen, C., Hyvärinen,
K., Ritari, J., Partanen, J., Ullum, H., Erikstrup, C.,
Ostrowski, S. R., Olsson, M. L., & Pedersen, O. B.
(2024). A deep learning approach to prediction of
blood group antigens from genomic data. Transfusion,
64(11), 2179–2195. https://doi.org/10.1111/trf.18013
21. Acquah MA, Chen N, Pan JS, Yang HM, Yan B.
Securing fingerprint template using blockchain and
distributed storage system. Symmetry [Internet].
2020 Jun 4;12(6):951. Available from: https://doi.
org/10.3390/sym12060951
22. Hassen OA, Abdulhussein AA, Darwish SM, Othman
ZA, Tiun S, Lotfy YA. Towards a secure signature
scheme based on multimodal biometric technology:
application for IOT Blockchain network. Symmetry
[Internet]. 2020 Oct 15;12(10):1699. Available from:
https://doi.org/10.3390/sym12101699
23. Liu M, Ma Z, Li Y, Yang P, Wang M, Liu Y, et al. A
comprehensive strategy integrating chemical profiling
with deep learning for screening the antithrombin
bioactive-chemical quality markers of functional
food: Pueraria radix as an example. LWT [Internet].
2024 Oct 1; 210:116876. Available from: https://doi.
org/10.1016/j.lwt.2024.116876
24. Sreehari S, Anzar SM. Touchless fingerprint
recognition: A survey of recent developments and
challenges. Computers & Electrical Engineering
[Internet]. 2024 Dec 13; 122:109894. Available from:
https://doi.org/10.1016/j.compeleceng.2024.109894
25. Hermans PW, Sluijter M, Hoogenboezem T, Heersma
H, Van Belkum A, De Groot R. Comparative study
of five different DNA fingerprint techniques for
molecular typing of Streptococcus pneumoniae
strains. Journal of Clinical Microbiology [Internet].
1995 Jun 1;33(6):1606–12. Available from: https://doi.
org/10.1128/jcm.33.6.1606-1612.1995
26. Europe PMC. Europe PMC [Internet]. Available from:
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