Smart medical report: efficient detection of common and rare diseases on common blood tests

Ákos Németh; Gábor Tóth; Péter Fülöp; György Paragh; Bíborka Nádró; Zsolt Karányi; György Paragh Jr; Zsolt Horváth; Zsolt Csernák; Erzsébet Pintér; Dániel Sándor; Gábor Bagyó; István Édes; János Kappelmayer; Mariann Harangi; Bálint Daróczy

doi:10.3389/fdgth.2024.1505483

Smart medical report: efficient detection of common and rare diseases on common blood tests

Front Digit Health. 2024 Dec 5:6:1505483. doi: 10.3389/fdgth.2024.1505483. eCollection 2024.

Authors

Ákos Németh^{1

2}, Gábor Tóth³, Péter Fülöp¹, György Paragh¹, Bíborka Nádró¹, Zsolt Karányi¹, György Paragh Jr⁴, Zsolt Horváth⁵, Zsolt Csernák^{6

7}, Erzsébet Pintér^{6

8}, Dániel Sándor^{2

9}, Gábor Bagyó¹⁰, István Édes¹¹, János Kappelmayer³, Mariann Harangi¹, Bálint Daróczy^{2

12}

Affiliations

¹ Division of Metabolic Diseases, Department of Internal Medicine, Faculty of Medicine, University of Debrecen, Debrecen, Hungary.
² Aesculab Medical Solutions, Black Horse Group Ltd., Debrecen, Hungary.
³ Department of Laboratory Medicine, Faculty of Medicine, University of Debrecen, Debrecen, Hungary.
⁴ Department of Dermatology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, United States.
⁵ Center of Oncoradiology, Bács-Kiskun County Teaching Hospital, Kecskemét, Hungary.
⁶ Central Medical Department, Synlab Group (Synlab Hungary Ltd.), Budapest, Hungary.
⁷ Central Laboratory, St. John's Hospital, Budapest, Hungary.
⁸ Laboratory of Immunology, Synlab Budapest Diagnostic Center, Budapest, Hungary.
⁹ Department of Artificial Intelligence and Systems Engineering, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Budapest, Hungary.
¹⁰ Evidia MVZ Radiologie, Nürnberg, Germany.
¹¹ Department of Cardiology, Faculty of Medicine, University of Debrecen, Debrecen, Hungary.
¹² Artificial Intelligence National Laboratory, Institute for Computer Science and Control (SZTAKI), Hungarian Research Network (HUN-REN), Budapest, Hungary.

Abstract

Introduction: The integration of AI into healthcare is widely anticipated to revolutionize medical diagnostics, enabling earlier, more accurate disease detection and personalized care.

Methods: In this study, we developed and validated an AI-assisted diagnostic support tool using only routinely ordered and broadly available blood tests to predict the presence of major chronic and acute diseases as well as rare disorders.

Results: Our model was tested on both retrospective and prospective datasets comprising over one million patients. We evaluated the diagnostic performance by (1) implementing ensemble learning (mean ROC-AUC.9293 and mean DOR 63.96); (2) assessing the model's sensitivity via risk scores to simulate its screening effectiveness; (3) analyzing the potential for early disease detection (30-270 days before clinical diagnosis) through creating historical patient timelines and (4) conducting validation on real-world clinical data in collaboration with Synlab Hungary, to assess the tool's performance in clinical setting.

Discussion: Uniquely, our model not only considers stable blood values but also tracks changes from baseline across 15 years of patient history. Our AI-driven automated diagnostic tool can significantly enhance clinical practice by recognizing patterns in common and rare diseases, including malignancies. The models' ability to detect diseases 1-9 months earlier than traditional clinical diagnosis could contribute to reduced healthcare costs and improved patient outcomes. The automated evaluation also reduces evaluation time of healthcare providers, which accelerates diagnostic processes. By utilizing only routine blood tests and ensemble methods, the tool demonstrates high efficacy across independent laboratories and hospitals, making it an exceptionally valuable screening resource for primary care physicians.

Keywords: blood test analysis; chronic diseases; classification; machine learning; prevention and control; rare diseases.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This research was funded by public channels. G. Toth received funding through the Society to Improve Diagnosis in Medicine's fellowship program, which is financially supported by the Gordon and Betty Moore Foundation. B. Daróczy received funding from the European Union project RRF-2.3.1-21-2022-00004 within the framework of the Artificial Intelligence National Laboratory and from the C.N.R.S. E.A.I. project “Stabilité des algorithmes d’apprentissage pour les réseaux de neurones profonds et récurrents en utilisant la géométrie et la théorie du contrôle via la compréhension du rôle de la surparamétrisation”. The study was supported by the National Research, Development, and Innovation Office (NKFIH, grant number: K142273). The funders played no role in the study design, data collection, analysis and interpretation of data, or the writing of this article. Synlab Hungary Ltd. was involved in the clinical testing as a testing partner and facilitator but it neither funded the project nor owns any IP related to it. Synlab Hungary provided validating clinical pathologists in their employment for the testing.