A regulatory science initiative to harmonize and standardize digital pathology and machine learning processes to speed up clinical innovation to patients
Unlocking the full potential of pathology data by gaining computational access to histological pixel data and metadata (digital pathology) is one of the key promises of computational pathology. Despite scientific progress and several regulatory approvals for primary diagnosis using whole-slide imagi...
| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2020-01-01
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| Series: | Journal of Pathology Informatics |
| Subjects: | |
| Online Access: | http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2020;volume=11;issue=1;spage=22;epage=22;aulast=Marble |
| _version_ | 1818209264872718336 |
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| author | Hetal Desai Marble Richard Huang Sarah Nixon Dudgeon Amanda Lowe Markus D Herrmann Scott Blakely Matthew O Leavitt Mike Isaacs Matthew G Hanna Ashish Sharma Jithesh Veetil Pamela Goldberg Joachim H Schmid Laura Lasiter Brandon D Gallas Esther Abels Jochen K Lennerz |
| author_facet | Hetal Desai Marble Richard Huang Sarah Nixon Dudgeon Amanda Lowe Markus D Herrmann Scott Blakely Matthew O Leavitt Mike Isaacs Matthew G Hanna Ashish Sharma Jithesh Veetil Pamela Goldberg Joachim H Schmid Laura Lasiter Brandon D Gallas Esther Abels Jochen K Lennerz |
| author_sort | Hetal Desai Marble |
| collection | DOAJ |
| description | Unlocking the full potential of pathology data by gaining computational access to histological pixel data and metadata (digital pathology) is one of the key promises of computational pathology. Despite scientific progress and several regulatory approvals for primary diagnosis using whole-slide imaging, true clinical adoption at scale is slower than anticipated. In the U.S., advances in digital pathology are often siloed pursuits by individual stakeholders, and to our knowledge, there has not been a systematic approach to advance the field through a regulatory science initiative. The Alliance for Digital Pathology ( the Alliance) is a recently established, volunteer, collaborative, regulatory science initiative to standardize digital pathology processes to speed up innovation to patients. The purpose is: (1) to account for the patient perspective by including patient advocacy; (2) to investigate and develop methods and tools for the evaluation of effectiveness, safety, and quality to specify risks and benefits in the precompetitive phase; (3) to help strategize the sequence of clinically meaningful deliverables; (4) to encourage and streamline the development of ground-truth data sets for machine learning model development and validation; and (5) to clarify regulatory pathways by investigating relevant regulatory science questions. The Alliance accepts participation from all stakeholders, and we solicit clinically relevant proposals that will benefit the field at large. The initiative will dissolve once a clinical, interoperable, modularized, integrated solution (from tissue acquisition to diagnostic algorithm) has been implemented. In times of rapidly evolving discoveries, scientific input from subject-matter experts is one essential element to inform regulatory guidance and decision-making. The Alliance aims to establish and promote synergistic regulatory science efforts that will leverage diverse inputs to move digital pathology forward and ultimately improve patient care. |
| first_indexed | 2024-12-12T04:57:57Z |
| format | Article |
| id | doaj.art-be50a3b9cd054f7984beeae023b6a521 |
| institution | Directory Open Access Journal |
| issn | 2153-3539 2153-3539 |
| language | English |
| last_indexed | 2024-12-12T04:57:57Z |
| publishDate | 2020-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Pathology Informatics |
| spelling | doaj.art-be50a3b9cd054f7984beeae023b6a5212022-12-22T00:37:18ZengElsevierJournal of Pathology Informatics2153-35392153-35392020-01-01111222210.4103/jpi.jpi_27_20A regulatory science initiative to harmonize and standardize digital pathology and machine learning processes to speed up clinical innovation to patientsHetal Desai MarbleRichard HuangSarah Nixon DudgeonAmanda LoweMarkus D HerrmannScott BlakelyMatthew O LeavittMike IsaacsMatthew G HannaAshish SharmaJithesh VeetilPamela GoldbergJoachim H SchmidLaura LasiterBrandon D GallasEsther AbelsJochen K LennerzUnlocking the full potential of pathology data by gaining computational access to histological pixel data and metadata (digital pathology) is one of the key promises of computational pathology. Despite scientific progress and several regulatory approvals for primary diagnosis using whole-slide imaging, true clinical adoption at scale is slower than anticipated. In the U.S., advances in digital pathology are often siloed pursuits by individual stakeholders, and to our knowledge, there has not been a systematic approach to advance the field through a regulatory science initiative. The Alliance for Digital Pathology ( the Alliance) is a recently established, volunteer, collaborative, regulatory science initiative to standardize digital pathology processes to speed up innovation to patients. The purpose is: (1) to account for the patient perspective by including patient advocacy; (2) to investigate and develop methods and tools for the evaluation of effectiveness, safety, and quality to specify risks and benefits in the precompetitive phase; (3) to help strategize the sequence of clinically meaningful deliverables; (4) to encourage and streamline the development of ground-truth data sets for machine learning model development and validation; and (5) to clarify regulatory pathways by investigating relevant regulatory science questions. The Alliance accepts participation from all stakeholders, and we solicit clinically relevant proposals that will benefit the field at large. The initiative will dissolve once a clinical, interoperable, modularized, integrated solution (from tissue acquisition to diagnostic algorithm) has been implemented. In times of rapidly evolving discoveries, scientific input from subject-matter experts is one essential element to inform regulatory guidance and decision-making. The Alliance aims to establish and promote synergistic regulatory science efforts that will leverage diverse inputs to move digital pathology forward and ultimately improve patient care.http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2020;volume=11;issue=1;spage=22;epage=22;aulast=Marbleartificial intelligencedigital pathologymachine learningregulatory scienceslide scanning |
| spellingShingle | Hetal Desai Marble Richard Huang Sarah Nixon Dudgeon Amanda Lowe Markus D Herrmann Scott Blakely Matthew O Leavitt Mike Isaacs Matthew G Hanna Ashish Sharma Jithesh Veetil Pamela Goldberg Joachim H Schmid Laura Lasiter Brandon D Gallas Esther Abels Jochen K Lennerz A regulatory science initiative to harmonize and standardize digital pathology and machine learning processes to speed up clinical innovation to patients Journal of Pathology Informatics artificial intelligence digital pathology machine learning regulatory science slide scanning |
| title | A regulatory science initiative to harmonize and standardize digital pathology and machine learning processes to speed up clinical innovation to patients |
| title_full | A regulatory science initiative to harmonize and standardize digital pathology and machine learning processes to speed up clinical innovation to patients |
| title_fullStr | A regulatory science initiative to harmonize and standardize digital pathology and machine learning processes to speed up clinical innovation to patients |
| title_full_unstemmed | A regulatory science initiative to harmonize and standardize digital pathology and machine learning processes to speed up clinical innovation to patients |
| title_short | A regulatory science initiative to harmonize and standardize digital pathology and machine learning processes to speed up clinical innovation to patients |
| title_sort | regulatory science initiative to harmonize and standardize digital pathology and machine learning processes to speed up clinical innovation to patients |
| topic | artificial intelligence digital pathology machine learning regulatory science slide scanning |
| url | http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2020;volume=11;issue=1;spage=22;epage=22;aulast=Marble |
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