AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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You can expand the Abstract, Links and BibTex record for each paper.
2025
Alibrahim, Y.; Ibrahim, M.; Gurdayal, D.; Munshi, M.
AI speechbots and 3D segmentations in virtual reality improve radiology on-call training in resource-limited settings Journal Article
In: Intelligence-Based Medicine, vol. 11, 2025, ISSN: 26665212 (ISSN).
Abstract | Links | BibTeX | Tags: 3D segmentation, AI speechbots, Article, artificial intelligence chatbot, ChatGPT, computer assisted tomography, Deep learning, headache, human, Image segmentation, interventional radiology, Large language model, Likert scale, nausea, Proof of concept, prospective study, radiology, radiology on call training, resource limited setting, Teaching, Training, ultrasound, Virtual Reality, voice recognition
@article{alibrahim_ai_2025,
title = {AI speechbots and 3D segmentations in virtual reality improve radiology on-call training in resource-limited settings},
author = {Y. Alibrahim and M. Ibrahim and D. Gurdayal and M. Munshi},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105001472313&doi=10.1016%2fj.ibmed.2025.100245&partnerID=40&md5=623a0ceaa07e5516a296420d25c3033b},
doi = {10.1016/j.ibmed.2025.100245},
issn = {26665212 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Intelligence-Based Medicine},
volume = {11},
abstract = {Objective: Evaluate the use of large-language model (LLM) speechbot tools and deep learning-assisted generation of 3D reconstructions when integrated in a virtual reality (VR) setting to teach radiology on-call topics to radiology residents. Methods: Three first year radiology residents in Guyana were enrolled in an 8-week radiology course that focused on preparation for on-call duties. The course, delivered via VR headsets with custom software integrating LLM-powered speechbots trained on imaging reports and 3D reconstructions segmented with the help of a deep learning model. Each session focused on a specific radiology area, employing a didactic and case-based learning approach, enhanced with 3D reconstructions and an LLM-powered speechbot. Post-session, residents reassessed their knowledge and provided feedback on their VR and LLM-powered speechbot experiences. Results/discussion: Residents found that the 3D reconstructions segmented semi-automatically by deep learning algorithms and AI-driven self-learning via speechbot was highly valuable. The 3D reconstructions, especially in the interventional radiology session, were helpful and the benefit is augmented by VR where navigating the models is seamless and perception of depth is pronounced. Residents also found conversing with the AI-speechbot seamless and was valuable in their post session self-learning. The major drawback of VR was motion sickness, which was mild and improved over time. Conclusion: AI-assisted VR radiology education could be used to develop new and accessible ways of teaching a variety of radiology topics in a seamless and cost-effective way. This could be especially useful in supporting radiology education remotely in regions which lack local radiology expertise. © 2025},
keywords = {3D segmentation, AI speechbots, Article, artificial intelligence chatbot, ChatGPT, computer assisted tomography, Deep learning, headache, human, Image segmentation, interventional radiology, Large language model, Likert scale, nausea, Proof of concept, prospective study, radiology, radiology on call training, resource limited setting, Teaching, Training, ultrasound, Virtual Reality, voice recognition},
pubstate = {published},
tppubtype = {article}
}
Objective: Evaluate the use of large-language model (LLM) speechbot tools and deep learning-assisted generation of 3D reconstructions when integrated in a virtual reality (VR) setting to teach radiology on-call topics to radiology residents. Methods: Three first year radiology residents in Guyana were enrolled in an 8-week radiology course that focused on preparation for on-call duties. The course, delivered via VR headsets with custom software integrating LLM-powered speechbots trained on imaging reports and 3D reconstructions segmented with the help of a deep learning model. Each session focused on a specific radiology area, employing a didactic and case-based learning approach, enhanced with 3D reconstructions and an LLM-powered speechbot. Post-session, residents reassessed their knowledge and provided feedback on their VR and LLM-powered speechbot experiences. Results/discussion: Residents found that the 3D reconstructions segmented semi-automatically by deep learning algorithms and AI-driven self-learning via speechbot was highly valuable. The 3D reconstructions, especially in the interventional radiology session, were helpful and the benefit is augmented by VR where navigating the models is seamless and perception of depth is pronounced. Residents also found conversing with the AI-speechbot seamless and was valuable in their post session self-learning. The major drawback of VR was motion sickness, which was mild and improved over time. Conclusion: AI-assisted VR radiology education could be used to develop new and accessible ways of teaching a variety of radiology topics in a seamless and cost-effective way. This could be especially useful in supporting radiology education remotely in regions which lack local radiology expertise. © 2025
2016
Abate, Andrea; Barra, Silvio; Gallo, Luigi; Narducci, Fabio
SKIPSOM: Skewness Amp; Kurtosis of Iris Pixels in Self Organizing Maps for Iris Recognition on Mobile Devices Proceedings Article
In: 2016 23rd International Conference on Pattern Recognition (ICPR), pp. 155–159, 2016.
Abstract | Links | BibTeX | Tags: Image segmentation, Iris, Mobile handsets, Self-organizing feature maps
@inproceedings{abateSKIPSOMSkewnessAmp2016,
title = {SKIPSOM: Skewness Amp; Kurtosis of Iris Pixels in Self Organizing Maps for Iris Recognition on Mobile Devices},
author = { Andrea Abate and Silvio Barra and Luigi Gallo and Fabio Narducci},
doi = {10.1109/ICPR.2016.7899625},
year = {2016},
date = {2016-12-01},
booktitle = {2016 23rd International Conference on Pattern Recognition (ICPR)},
pages = {155--159},
abstract = {In the last fifteen years, smartphones have become very popular amongst the population, with the subsequent development of dozens of applications aimed at providing security to these portable devices. Nowadays, the cutting edge devices are also provided with biometric sensors (e.g., fingerprint sensors) allowing the users to access them without using the out-of-date alphanumerical password. In this work, we present a method that realizes iris recognition by means of Self Organizing Maps (SOM). In order to obtain a better refined and discriminative feature map, the RGB data of the iris, previously segmented, have been combined with two statistical descriptors. The algorithm has been designed specifically to require a low processing power, making it an ideal choice in the context of mobile devices.},
keywords = {Image segmentation, Iris, Mobile handsets, Self-organizing feature maps},
pubstate = {published},
tppubtype = {inproceedings}
}
In the last fifteen years, smartphones have become very popular amongst the population, with the subsequent development of dozens of applications aimed at providing security to these portable devices. Nowadays, the cutting edge devices are also provided with biometric sensors (e.g., fingerprint sensors) allowing the users to access them without using the out-of-date alphanumerical password. In this work, we present a method that realizes iris recognition by means of Self Organizing Maps (SOM). In order to obtain a better refined and discriminative feature map, the RGB data of the iris, previously segmented, have been combined with two statistical descriptors. The algorithm has been designed specifically to require a low processing power, making it an ideal choice in the context of mobile devices.
Abate, Andrea; Barra, Silvio; Gallo, Luigi; Narducci, Fabio
SKIPSOM: Skewness amp; kurtosis of iris pixels in Self Organizing Maps for iris recognition on mobile devices Proceedings Article
In: 2016 23rd International Conference on Pattern Recognition (ICPR), pp. 155–159, 2016.
Abstract | Links | BibTeX | Tags: Image segmentation, Iris, Mobile handsets, Self-organizing feature maps
@inproceedings{abate_skipsom_2016,
title = {SKIPSOM: Skewness amp; kurtosis of iris pixels in Self Organizing Maps for iris recognition on mobile devices},
author = {Andrea Abate and Silvio Barra and Luigi Gallo and Fabio Narducci},
doi = {10.1109/ICPR.2016.7899625},
year = {2016},
date = {2016-12-01},
booktitle = {2016 23rd International Conference on Pattern Recognition (ICPR)},
pages = {155–159},
abstract = {In the last fifteen years, smartphones have become very popular amongst the population, with the subsequent development of dozens of applications aimed at providing security to these portable devices. Nowadays, the cutting edge devices are also provided with biometric sensors (e.g., fingerprint sensors) allowing the users to access them without using the out-of-date alphanumerical password. In this work, we present a method that realizes iris recognition by means of Self Organizing Maps (SOM). In order to obtain a better refined and discriminative feature map, the RGB data of the iris, previously segmented, have been combined with two statistical descriptors. The algorithm has been designed specifically to require a low processing power, making it an ideal choice in the context of mobile devices.},
keywords = {Image segmentation, Iris, Mobile handsets, Self-organizing feature maps},
pubstate = {published},
tppubtype = {inproceedings}
}
In the last fifteen years, smartphones have become very popular amongst the population, with the subsequent development of dozens of applications aimed at providing security to these portable devices. Nowadays, the cutting edge devices are also provided with biometric sensors (e.g., fingerprint sensors) allowing the users to access them without using the out-of-date alphanumerical password. In this work, we present a method that realizes iris recognition by means of Self Organizing Maps (SOM). In order to obtain a better refined and discriminative feature map, the RGB data of the iris, previously segmented, have been combined with two statistical descriptors. The algorithm has been designed specifically to require a low processing power, making it an ideal choice in the context of mobile devices.