Current status of machine learning in thyroid cytopathology
The implementation of Digital Pathology has allowed the development of computational Pathology. Digital image-based applications that have received FDA Breakthrough Device Designation have been primarily focused on tissue specimens. The development of Artificial Intelligence-assisted algorithms usin...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-01-01
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| Series: | Journal of Pathology Informatics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2153353923001232 |
| _version_ | 1827970136216174592 |
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| author | Charles M. Wong Brie E. Kezlarian Oscar Lin |
| author_facet | Charles M. Wong Brie E. Kezlarian Oscar Lin |
| author_sort | Charles M. Wong |
| collection | DOAJ |
| description | The implementation of Digital Pathology has allowed the development of computational Pathology. Digital image-based applications that have received FDA Breakthrough Device Designation have been primarily focused on tissue specimens. The development of Artificial Intelligence-assisted algorithms using Cytology digital images has been much more limited due to technical challenges and a lack of optimized scanners for Cytology specimens. Despite the challenges in scanning whole slide images of cytology specimens, there have been many studies evaluating CP to create decision-support tools in Cytopathology. Among different Cytology specimens, thyroid fine needle aspiration biopsy (FNAB) specimens have one of the greatest potentials to benefit from machine learning algorithms (MLA) derived from digital images. Several authors have evaluated different machine learning algorithms focused on thyroid cytology in the past few years. The results are promising. The algorithms have mostly shown increased accuracy in the diagnosis and classification of thyroid cytology specimens. They have brought new insights and demonstrated the potential for improving future cytopathology workflow efficiency and accuracy. However, many issues still need to be addressed to further build on and improve current MLA models and their applications. To optimally train and validate MLA for thyroid cytology specimens, larger datasets obtained from multiple institutions are needed. MLAs hold great potential in improving thyroid cancer diagnostic speed and accuracy that will lead to improvements in patient management. |
| first_indexed | 2024-04-09T18:49:20Z |
| format | Article |
| id | doaj.art-3717bd1af0624f52a782682b8df1fb27 |
| institution | Directory Open Access Journal |
| issn | 2153-3539 |
| language | English |
| last_indexed | 2024-04-09T18:49:20Z |
| publishDate | 2023-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Pathology Informatics |
| spelling | doaj.art-3717bd1af0624f52a782682b8df1fb272023-04-10T04:03:47ZengElsevierJournal of Pathology Informatics2153-35392023-01-0114100309Current status of machine learning in thyroid cytopathologyCharles M. Wong0Brie E. Kezlarian1Oscar Lin2Memorial Sloan-Kettering Cancer Center, New York, NY, USAMemorial Sloan-Kettering Cancer Center, New York, NY, USACorresponding author at: Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA.; Memorial Sloan-Kettering Cancer Center, New York, NY, USAThe implementation of Digital Pathology has allowed the development of computational Pathology. Digital image-based applications that have received FDA Breakthrough Device Designation have been primarily focused on tissue specimens. The development of Artificial Intelligence-assisted algorithms using Cytology digital images has been much more limited due to technical challenges and a lack of optimized scanners for Cytology specimens. Despite the challenges in scanning whole slide images of cytology specimens, there have been many studies evaluating CP to create decision-support tools in Cytopathology. Among different Cytology specimens, thyroid fine needle aspiration biopsy (FNAB) specimens have one of the greatest potentials to benefit from machine learning algorithms (MLA) derived from digital images. Several authors have evaluated different machine learning algorithms focused on thyroid cytology in the past few years. The results are promising. The algorithms have mostly shown increased accuracy in the diagnosis and classification of thyroid cytology specimens. They have brought new insights and demonstrated the potential for improving future cytopathology workflow efficiency and accuracy. However, many issues still need to be addressed to further build on and improve current MLA models and their applications. To optimally train and validate MLA for thyroid cytology specimens, larger datasets obtained from multiple institutions are needed. MLAs hold great potential in improving thyroid cancer diagnostic speed and accuracy that will lead to improvements in patient management.http://www.sciencedirect.com/science/article/pii/S2153353923001232Computational pathologyDigital pathologyMachine learning algorithmsThyroidCytology |
| spellingShingle | Charles M. Wong Brie E. Kezlarian Oscar Lin Current status of machine learning in thyroid cytopathology Journal of Pathology Informatics Computational pathology Digital pathology Machine learning algorithms Thyroid Cytology |
| title | Current status of machine learning in thyroid cytopathology |
| title_full | Current status of machine learning in thyroid cytopathology |
| title_fullStr | Current status of machine learning in thyroid cytopathology |
| title_full_unstemmed | Current status of machine learning in thyroid cytopathology |
| title_short | Current status of machine learning in thyroid cytopathology |
| title_sort | current status of machine learning in thyroid cytopathology |
| topic | Computational pathology Digital pathology Machine learning algorithms Thyroid Cytology |
| url | http://www.sciencedirect.com/science/article/pii/S2153353923001232 |
| work_keys_str_mv | AT charlesmwong currentstatusofmachinelearninginthyroidcytopathology AT brieekezlarian currentstatusofmachinelearninginthyroidcytopathology AT oscarlin currentstatusofmachinelearninginthyroidcytopathology |