Sid Singh²*, Reham Ahmad^1,2 Kimaya Garg², Mena Kumari², Paolo Melissa³ and Manoj Srivastava^2,4

¹University of Warwick, Warwick Medical School (WMS), Coventry, England CV4 7AL, UK
²Department of Clinical Informatics, George Eliot Hospital, College Street, Nuneaton, Warwickshire, England CV10 7DJ, UK
³Department of Information, George Eliot Hospital, College Street, Nuneaton, Warwickshire, England CV10 7DJ, UK
⁴Department of Radiology, George Eliot Hospital, College Street, Nuneaton, Warwickshire, England CV10 7DJ, UK

*Corresponding Author: Sid Singh, Department of Clinical Informatics, George Eliot Hospital, College Street, Nuneaton, Warwickshire, England CV10 7DJ, UK.

Received: June 11, 2025; Published: July 27, 2025

Abstract

Manual analysis of lesions in serial Computed Tomography (CT) scans is often performed in 2D, making it time-consuming and error-prone. There is a growing need for automated, explainable tools that support radiologists in clinical environments. This study presents a novel, human-centric Artificial Intelligence (AI) framework that combines classical computer vision for automatic alignment, machine learning and deep learning for tissue sectioning, and unsupervised learning techniques for lesion detection. These were used to create a Proof-of-Concept Graphical User Interface tool. Developed over a 12-week period in collaboration with George Eliot Hospital (GEH) and ROKE as part of the National Health Service (NHS) AI Skunkworks programme. The proposed framework offers a novel contribution by integrating explainable AI techniques with human-centred design to improve CT scan analysis. It advances current research by focusing on clinical usability, transparency, and co-development with healthcare professionals.

Keywords: Human-Centred AI; Alignment; Lesion Detection; CT Comparison; Human Health

References

1. S Neethirajan. “Artificial Intelligence and Sensor Innovations: Enhancing Livestock Welfare with a Human-Centric Approach”. Human-Centric Intelligent Systems (2023): 1-16.
2. A Bohr and K “The rise of artificial intelligence in healthcare applications”. In Artificial Intelligence in healthcare (2020): 25-60.
3. J Zhang and ZZ Zhang. “Ethics and governance of trustworthy medical artificial intelligence”. BMC Medical Informatics and Decision Making (2023): 1-7.
4. D KUMAR., et al. “Integrating artificial intelligence in disease diagnosis, treatment, and formulation development: a review”. Asian Journal of Pharmaceutical and Clinical Research (2023): 1-8.
5. W Xu., et al. “Transitioning to human interaction with AI systems: New challenges and opportunities for HCI professionals to enable human-centered AI”. International Journal of Human–Computer Interaction (2023): 494-518.
6. H Chen., et al. “Explainable medical imaging AI needs human-centered design: guidelines and evidence from a systematic review”. NPJ Digital Medicine (2022): 156.
7. H , et al. “Extension of phase correlation to subpixel registration”. IEEE transactions on image processing (2002): 188-200.
8. A Myronenko and M A Song. “Point Set Registration: Coherent Point Drift”. IEEE Transactions on Pattern Analysis and Machine Intelligence (2010): 2262-2275.
9. J Malik., et al. “Contour and texture analysis for image segmentation”. International Journal of Computer Vision (2001): 7-27.
10. M Caron., et al. “Emerging properties in self-supervised vision transformers”. In Proceedings of the IEEE/CVF international conference on computer vision (2021): 9650-9660.
11. G Bradski. “Finding contours in your image”. (2023).
12. K Shrimali. “Convex Hull using OpenCV in Python and C++”.
13. (n.d).
14. Pang G., et al. “Learning for anomaly detection: A review”. ACM Computing Surveys (CSUR) (2021): 1-38.
15. FM Calisto., et al. “Introduction of human-centric AI assistant to aid radiologists for multimodal breast image classification”. International Journal of Human-Computer Studies (2021): 102607.
16. Rublee E., et al. “ORB: An efficient alternative to SIFT or SURF”. In 2011 International conference on computer vision (2011): 2564-2571.
17. A Wallisch., et al. “Overcoming fuzzy design practice: revealing potentials of user-centered design research and methodological concepts related to user involvement”. In 2019 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC): (2029): 1-9.
18. M Sharifi., et al. “A Classified and Comparative Study of Edge Detection Algorithms”. International conference on information technology: Coding and computing (2002): 1s-120.
19. D G Lowe. “Object recognition from local scale-invariant features”. International Conference on Computer Vision (1999): 1150-1157.
20. C Malamateniou., et al. “Artificial intelligence in radiography: where are we now and what does the future hold?” Radiography (2021): S58-S62.

Citation

Citation: Sid Singh., et al. “Using INTRPRT Guideline to Assess Human-Centric AI Design in CT Tissue Growth Detection". Acta Scientific Clinical Case Reports 6.8 (2025): 13-24.

Copyright

Copyright: © 2025 Sid Singh., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.