Clinical Validation of AI-Powered Coagulation Panel Interpretation: Multi-Parameter Analysis for Enhanced Diagnostic Accuracy in Thrombosis and Bleeding Disorder Assessment

This peer-reviewed research article presents the clinical validation of an artificial intelligence (AI) system for automated coagulation panel interpretation, developed by Kantesti AI. The study validates a proprietary 2.78 trillion parameter neural network architecture specifically designed for multi-parameter laboratory test analysis, focusing on critical coagulation biomarkers including activated partial thromboplastin time (aPTT), prothrombin time (PT/INR), D-Dimer, Protein C, Protein S, and free light chain assays. STUDY OVERVIEW Blood coagulation disorders—encompassing both thrombotic conditions such as deep vein thrombosis (DVT) and pulmonary embolism (PE), as well as hemorrhagic conditions resulting from clotting factor deficiencies—represent a significant global health burden. Accurate interpretation of coagulation panel results is essential for appropriate diagnosis, risk stratification, and clinical management. However, the complexity of multi-parameter coagulation analysis creates interpretive challenges, particularly in resource-limited settings where specialist hematology expertise may be unavailable. This multi-center retrospective validation study analyzed 652,847 complete coagulation panel results from patients across 127 countries between January 2024 and December 2025. The AI system's interpretations were compared against consensus assessments from board-certified hematologists using a rigorous triple-blind methodology to eliminate confirmation bias and ensure objective accuracy measurement. KEY FINDINGS - Overall Diagnostic Accuracy: 98.4% (95% CI: 98.1–98.7%)- Thrombosis Risk Assessment: Sensitivity 98.9%, Specificity 97.6%- Bleeding Disorder Detection: Sensitivity 97.4%, Specificity 98.8%- AI-Physician Concordance: Cohen's kappa = 0.94 (near-perfect agreement)- Turnaround Time Reduction: 94.2% faster than traditional expert review (47 seconds vs. 13.5 minutes) CLINICAL PARAMETERS VALIDATED The AI system demonstrated high accuracy across all major coagulation parameters:- aPTT (Activated Partial Thromboplastin Time): 99.2% accuracy for intrinsic pathway assessment- PT/INR (Prothrombin Time): 98.7% accuracy for extrinsic pathway and anticoagulant monitoring- D-Dimer: 98.9% accuracy for fibrinolysis and thrombotic activity detection- Protein C: 96.8% accuracy for natural anticoagulant deficiency identification- Protein S: 96.4% accuracy for thrombophilia screening- Kappa/Lambda Ratio: 97.1% accuracy for plasma cell disorder screening IMPLICATIONS FOR CLINICAL PRACTICE This validation study demonstrates that AI-powered coagulation panel interpretation can achieve clinical-grade diagnostic accuracy comparable to expert hematologist consensus while providing transformative improvements in efficiency. The technology offers significant potential for: - Democratizing access to expert-level diagnostic support in resource-limited healthcare settings- Accelerating time-sensitive clinical decisions in emergency departments- Enabling high-throughput laboratory workflow optimization- Supporting clinical decision-making for anticoagulation therapy management METHODOLOGY The study employed a triple-blind validation methodology where: (1) the AI system interpreted coagulation panels without clinical context, (2) three independent board-certified hematologists reviewed panels without seeing AI output, and (3) a separate team compared results without knowing which interpretations came from AI versus physicians. This rigorous approach ensures unbiased accuracy assessment. TECHNOLOGY PLATFORM The Kantesti AI Blood Test Analyzer (https://www.kantesti.net) utilizes advanced deep learning architecture with multi-head attention mechanisms for simultaneous evaluation of parameter inter-relationships. The platform is CE Marked, HIPAA compliant, and GDPR compliant, serving over 2 million users across 127+ countries in 75+ languages. RESEARCH PARTNERSHIPS This research was conducted with technical support from Microsoft for Startups Founders Hub, NVIDIA Inception Program, and Google Cloud Partner Program. AUTHORS - Thomas Klein, MD – Chief Medical Officer, Kantesti AI- Prof. Dr. Hans Weber, PhD – Kantesti AI Medical Advisory Board- Dr. Sarah Mitchell, MD, PhD – Kantesti AI Medical Advisory Board RELATED RESOURCES - Educational Article: https://www.kantesti.net/coagulation-tests-aptt-protein-c-d-dimer-guide/- AI Blood Test Analyzer: https://www.kantesti.net- Medical Validation Documentation: https://www.kantesti.net/medical-validation/ LICENSE This article is distributed under Creative Commons Attribution 4.0 International (CC BY 4.0), allowing unrestricted use, distribution, and reproduction provided the original work is properly cited. CITATION Klein T, Weber H, Mitchell S. Clinical Validation of AI-Powered Coagulation Panel Interpretation: Multi-Parameter Analysis for Enhanced Diagnostic Accuracy in Thrombosis and Bleeding Disorder Assessment. J Clin Hematol AI Diagn. 2026;3:18262555. doi:10.5281/zenodo.18262555