Towards Machine Learning-Aided Lung Cancer Clinical Routines: Approaches and Open Challenges
Advancements in the development of computer-aided decision (CAD) systems for clinical routines provide unquestionable benefits in connecting human medical expertise with machine intelligence, to achieve better quality healthcare. Considering the large number of incidences and mortality numbers assoc...
| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-03-01
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| Series: | Journal of Personalized Medicine |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4426/12/3/480 |
| _version_ | 1797446318988197888 |
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| author | Francisco Silva Tania Pereira Inês Neves Joana Morgado Cláudia Freitas Mafalda Malafaia Joana Sousa João Fonseca Eduardo Negrão Beatriz Flor de Lima Miguel Correia da Silva António J. Madureira Isabel Ramos José Luis Costa Venceslau Hespanhol António Cunha Hélder P. Oliveira |
| author_facet | Francisco Silva Tania Pereira Inês Neves Joana Morgado Cláudia Freitas Mafalda Malafaia Joana Sousa João Fonseca Eduardo Negrão Beatriz Flor de Lima Miguel Correia da Silva António J. Madureira Isabel Ramos José Luis Costa Venceslau Hespanhol António Cunha Hélder P. Oliveira |
| author_sort | Francisco Silva |
| collection | DOAJ |
| description | Advancements in the development of computer-aided decision (CAD) systems for clinical routines provide unquestionable benefits in connecting human medical expertise with machine intelligence, to achieve better quality healthcare. Considering the large number of incidences and mortality numbers associated with lung cancer, there is a need for the most accurate clinical procedures; thus, the possibility of using artificial intelligence (AI) tools for decision support is becoming a closer reality. At any stage of the lung cancer clinical pathway, specific obstacles are identified and “motivate” the application of innovative AI solutions. This work provides a comprehensive review of the most recent research dedicated toward the development of CAD tools using computed tomography images for lung cancer-related tasks. We discuss the major challenges and provide critical perspectives on future directions. Although we focus on lung cancer in this review, we also provide a more clear definition of the path used to integrate AI in healthcare, emphasizing fundamental research points that are crucial for overcoming current barriers. |
| first_indexed | 2024-03-09T13:39:52Z |
| format | Article |
| id | doaj.art-ec6e78f8897b4933966c8a656cbec8b1 |
| institution | Directory Open Access Journal |
| issn | 2075-4426 |
| language | English |
| last_indexed | 2024-03-09T13:39:52Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Personalized Medicine |
| spelling | doaj.art-ec6e78f8897b4933966c8a656cbec8b12023-11-30T21:09:04ZengMDPI AGJournal of Personalized Medicine2075-44262022-03-0112348010.3390/jpm12030480Towards Machine Learning-Aided Lung Cancer Clinical Routines: Approaches and Open ChallengesFrancisco Silva0Tania Pereira1Inês Neves2Joana Morgado3Cláudia Freitas4Mafalda Malafaia5Joana Sousa6João Fonseca7Eduardo Negrão8Beatriz Flor de Lima9Miguel Correia da Silva10António J. Madureira11Isabel Ramos12José Luis Costa13Venceslau Hespanhol14António Cunha15Hélder P. Oliveira16INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, PortugalINESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, PortugalINESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, PortugalINESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, PortugalCHUSJ—Centro Hospitalar e Universitário de São João, 4200-319 Porto, PortugalINESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, PortugalINESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, PortugalINESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, PortugalCHUSJ—Centro Hospitalar e Universitário de São João, 4200-319 Porto, PortugalCHUSJ—Centro Hospitalar e Universitário de São João, 4200-319 Porto, PortugalCHUSJ—Centro Hospitalar e Universitário de São João, 4200-319 Porto, PortugalCHUSJ—Centro Hospitalar e Universitário de São João, 4200-319 Porto, PortugalCHUSJ—Centro Hospitalar e Universitário de São João, 4200-319 Porto, PortugalFMUP—Faculty of Medicine, University of Porto, 4200-319 Porto, PortugalCHUSJ—Centro Hospitalar e Universitário de São João, 4200-319 Porto, PortugalINESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, PortugalINESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, PortugalAdvancements in the development of computer-aided decision (CAD) systems for clinical routines provide unquestionable benefits in connecting human medical expertise with machine intelligence, to achieve better quality healthcare. Considering the large number of incidences and mortality numbers associated with lung cancer, there is a need for the most accurate clinical procedures; thus, the possibility of using artificial intelligence (AI) tools for decision support is becoming a closer reality. At any stage of the lung cancer clinical pathway, specific obstacles are identified and “motivate” the application of innovative AI solutions. This work provides a comprehensive review of the most recent research dedicated toward the development of CAD tools using computed tomography images for lung cancer-related tasks. We discuss the major challenges and provide critical perspectives on future directions. Although we focus on lung cancer in this review, we also provide a more clear definition of the path used to integrate AI in healthcare, emphasizing fundamental research points that are crucial for overcoming current barriers.https://www.mdpi.com/2075-4426/12/3/480computer-aided decisionlearning modelsCT scanlung cancer |
| spellingShingle | Francisco Silva Tania Pereira Inês Neves Joana Morgado Cláudia Freitas Mafalda Malafaia Joana Sousa João Fonseca Eduardo Negrão Beatriz Flor de Lima Miguel Correia da Silva António J. Madureira Isabel Ramos José Luis Costa Venceslau Hespanhol António Cunha Hélder P. Oliveira Towards Machine Learning-Aided Lung Cancer Clinical Routines: Approaches and Open Challenges Journal of Personalized Medicine computer-aided decision learning models CT scan lung cancer |
| title | Towards Machine Learning-Aided Lung Cancer Clinical Routines: Approaches and Open Challenges |
| title_full | Towards Machine Learning-Aided Lung Cancer Clinical Routines: Approaches and Open Challenges |
| title_fullStr | Towards Machine Learning-Aided Lung Cancer Clinical Routines: Approaches and Open Challenges |
| title_full_unstemmed | Towards Machine Learning-Aided Lung Cancer Clinical Routines: Approaches and Open Challenges |
| title_short | Towards Machine Learning-Aided Lung Cancer Clinical Routines: Approaches and Open Challenges |
| title_sort | towards machine learning aided lung cancer clinical routines approaches and open challenges |
| topic | computer-aided decision learning models CT scan lung cancer |
| url | https://www.mdpi.com/2075-4426/12/3/480 |
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