Artificial Intelligence Techniques and Pedigree Charts in Oncogenetics: Towards an Experimental Multioutput Software System for Digitization and Risk Prediction
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...
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MDPI AG
2024-03-01
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| Series: | Computation |
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| Online Access: | https://www.mdpi.com/2079-3197/12/3/47 |
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| author | Luana Conte Emanuele Rizzo Tiziana Grassi Francesco Bagordo Elisabetta De Matteis Giorgio De Nunzio |
| author_facet | Luana Conte Emanuele Rizzo Tiziana Grassi Francesco Bagordo Elisabetta De Matteis Giorgio De Nunzio |
| author_sort | Luana Conte |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T18:25:13Z |
| format | Article |
| id | doaj.art-03b2876e418f40228e04be0e356f11be |
| institution | Directory Open Access Journal |
| issn | 2079-3197 |
| language | English |
| last_indexed | 2024-04-24T18:25:13Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computation |
| spelling | doaj.art-03b2876e418f40228e04be0e356f11be2024-03-27T13:31:54ZengMDPI AGComputation2079-31972024-03-011234710.3390/computation12030047Artificial Intelligence Techniques and Pedigree Charts in Oncogenetics: Towards an Experimental Multioutput Software System for Digitization and Risk PredictionLuana Conte0Emanuele Rizzo1Tiziana Grassi2Francesco Bagordo3Elisabetta De Matteis4Giorgio De Nunzio5Laboratory of Biomedical Physics and Environment, Department of Mathematics and Physics “E. De Giorgi”, University of Salento, 73100 Lecce, ItalyDepartment of Biological and Environmental Sciences and Technologies, University of Salento, 73100 Lecce, ItalyDepartment of Experimental Medicine, University of Salento, 73100 Lecce, ItalyDepartment of Pharmacy-Pharmaceutical Sciences, University of Bari “Aldo Moro”, 70124 Bari, ItalyOncological Screenings Unit, Local Health Authority of Lecce, 73100 Lecce, ItalyLaboratory of Biomedical Physics and Environment, Department of Mathematics and Physics “E. De Giorgi”, University of Salento, 73100 Lecce, ItalyPedigree 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.https://www.mdpi.com/2079-3197/12/3/47artificial intelligencemachine learningdeep learningpedigree chartsoncogeneticsoncological genetic counseling |
| spellingShingle | Luana Conte Emanuele Rizzo Tiziana Grassi Francesco Bagordo Elisabetta De Matteis Giorgio De Nunzio Artificial Intelligence Techniques and Pedigree Charts in Oncogenetics: Towards an Experimental Multioutput Software System for Digitization and Risk Prediction Computation artificial intelligence machine learning deep learning pedigree charts oncogenetics oncological genetic counseling |
| title | Artificial Intelligence Techniques and Pedigree Charts in Oncogenetics: Towards an Experimental Multioutput Software System for Digitization and Risk Prediction |
| title_full | Artificial Intelligence Techniques and Pedigree Charts in Oncogenetics: Towards an Experimental Multioutput Software System for Digitization and Risk Prediction |
| title_fullStr | Artificial Intelligence Techniques and Pedigree Charts in Oncogenetics: Towards an Experimental Multioutput Software System for Digitization and Risk Prediction |
| title_full_unstemmed | Artificial Intelligence Techniques and Pedigree Charts in Oncogenetics: Towards an Experimental Multioutput Software System for Digitization and Risk Prediction |
| title_short | Artificial Intelligence Techniques and Pedigree Charts in Oncogenetics: Towards an Experimental Multioutput Software System for Digitization and Risk Prediction |
| title_sort | artificial intelligence techniques and pedigree charts in oncogenetics towards an experimental multioutput software system for digitization and risk prediction |
| topic | artificial intelligence machine learning deep learning pedigree charts oncogenetics oncological genetic counseling |
| url | https://www.mdpi.com/2079-3197/12/3/47 |
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