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Computation
Volume 12
Issue 3
10.3390/computation12030047
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Study Protocol

Artificial Intelligence Techniques and Pedigree Charts in Oncogenetics: Towards an Experimental Multioutput Software System for Digitization and Risk Prediction

by
Luana Conte
1,2,
Emanuele Rizzo
3,*,
Tiziana Grassi
4,
Francesco Bagordo
5,
Elisabetta De Matteis
6 and
Giorgio De Nunzio
1,2
1
Laboratory of Biomedical Physics and Environment, Department of Mathematics and Physics “E. De Giorgi”, University of Salento, 73100 Lecce, Italy
2
Laboratory of Advanced Data Analysis for Medicine (ADAM), Laboratory of Interdisciplinary Research Applied to Medicine, University of Salento/Local Health Authority of Lecce, 73100 Lecce, Italy
3
Department of Biological and Environmental Sciences and Technologies, University of Salento, 73100 Lecce, Italy
4
Department of Experimental Medicine, University of Salento, 73100 Lecce, Italy
5
Department of Pharmacy-Pharmaceutical Sciences, University of Bari “Aldo Moro”, 70124 Bari, Italy
6
Oncological Screenings Unit, Local Health Authority of Lecce, 73100 Lecce, Italy
*
Author to whom correspondence should be addressed.
Computation 2024, 12(3), 47; https://doi.org/10.3390/computation12030047
Submission received: 8 January 2024 / Revised: 28 February 2024 / Accepted: 29 February 2024 / Published: 3 March 2024
(This article belongs to the Special Issue Computational Biology and High-Performance Computing)
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Abstract

Pedigree charts remain essential in oncological genetic counseling for identifying individuals with an increased risk of developing hereditary tumors. However, this valuable data source often remains confined to paper files, going unused. We propose a computer-aided detection/diagnosis system, based on machine learning and deep learning techniques, capable of the following: (1) assisting genetic oncologists in digitizing paper-based pedigree charts, and in generating new digital ones, and (2) automatically predicting the genetic predisposition risk directly from these digital pedigree charts. To the best of our knowledge, there are no similar studies in the current literature, and consequently, no utilization of software based on artificial intelligence on pedigree charts has been made public yet. By incorporating medical images and other data from omics sciences, there is also a fertile ground for training additional artificial intelligence systems, broadening the software predictive capabilities. We plan to bridge the gap between scientific advancements and practical implementation by modernizing and enhancing existing oncological genetic counseling services. This would mark the pioneering development of an AI-based application designed to enhance various aspects of genetic counseling, leading to improved patient care and advancements in the field of oncogenetics.
Keywords: artificial intelligence; machine learning; deep learning; pedigree charts; oncogenetics; oncological genetic counseling artificial intelligence; machine learning; deep learning; pedigree charts; oncogenetics; oncological genetic counseling

Share and Cite

MDPI and ACS Style

Conte, L.; Rizzo, E.; Grassi, T.; Bagordo, F.; De Matteis, E.; De Nunzio, G. Artificial Intelligence Techniques and Pedigree Charts in Oncogenetics: Towards an Experimental Multioutput Software System for Digitization and Risk Prediction. Computation 2024, 12, 47. https://doi.org/10.3390/computation12030047

AMA Style

Conte L, Rizzo E, Grassi T, Bagordo F, De Matteis E, De Nunzio G. Artificial Intelligence Techniques and Pedigree Charts in Oncogenetics: Towards an Experimental Multioutput Software System for Digitization and Risk Prediction. Computation. 2024; 12(3):47. https://doi.org/10.3390/computation12030047

Chicago/Turabian Style

Conte, Luana, Emanuele Rizzo, Tiziana Grassi, Francesco Bagordo, Elisabetta De Matteis, and Giorgio De Nunzio. 2024. "Artificial Intelligence Techniques and Pedigree Charts in Oncogenetics: Towards an Experimental Multioutput Software System for Digitization and Risk Prediction" Computation 12, no. 3: 47. https://doi.org/10.3390/computation12030047

APA Style

Conte, L., Rizzo, E., Grassi, T., Bagordo, F., De Matteis, E., & De Nunzio, G. (2024). Artificial Intelligence Techniques and Pedigree Charts in Oncogenetics: Towards an Experimental Multioutput Software System for Digitization and Risk Prediction. Computation, 12(3), 47. https://doi.org/10.3390/computation12030047

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