Artificial Intelligence-Based Quality Assessment of Histopathology Whole-Slide Images within a Clinical Workflow: Assessment of ‘PathProfiler’ in a Diagnostic Pathology Setting Article Swipe
Lisa Browning
,
Christine Jesus
,
Stefano Malacrino
,
Yue Guan
,
Kieron White
,
Alison Puddle
,
Nasullah Khalid Alham
,
Maryam Haghighat
,
Richard Colling
,
Jacqueline Birks
,
Jens Rittscher
,
Clare Verrill
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/diagnostics14100990
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/diagnostics14100990
Digital pathology continues to gain momentum, with the promise of artificial intelligence to aid diagnosis and for assessment of features which may impact prognosis and clinical management. Successful adoption of these technologies depends upon the quality of digitised whole-slide images (WSI); however, current quality control largely depends upon manual assessment, which is inefficient and subjective. We previously developed PathProfiler, an automated image quality assessment tool, and in this feasibility study we investigate its potential for incorporation into a diagnostic clinical pathology setting in real-time. A total of 1254 genitourinary WSI were analysed by PathProfiler. PathProfiler was developed and trained on prostate tissue and, of the prostate biopsy WSI, representing 46% of the WSI analysed, 4.5% were flagged as potentially being of suboptimal quality for diagnosis. All had concordant subjective issues, mainly focus-related, 54% severe enough to warrant remedial action which resulted in improved image quality. PathProfiler was less reliable in assessment of non-prostate surgical resection-type cases, on which it had not been trained. PathProfiler shows potential for incorporation into a digitised clinical pathology workflow, with opportunity for image quality improvement. Whilst its reliability in the current form appears greatest for assessment of prostate specimens, other specimen types, particularly biopsies, also showed benefit.
Related Topics
Concepts
Digital pathology
Workflow
Medical physics
Quality assessment
Medicine
Prostate
Quality (philosophy)
Prostate biopsy
Reliability (semiconductor)
Pathology
Artificial intelligence
Computer science
External quality assessment
Internal medicine
Philosophy
Database
Physics
Quantum mechanics
Cancer
Power (physics)
Epistemology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/diagnostics14100990
- https://www.mdpi.com/2075-4418/14/10/990/pdf?version=1715225498
- OA Status
- gold
- Cited By
- 8
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4396773893
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4396773893Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/diagnostics14100990Digital Object Identifier
- Title
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Artificial Intelligence-Based Quality Assessment of Histopathology Whole-Slide Images within a Clinical Workflow: Assessment of ‘PathProfiler’ in a Diagnostic Pathology SettingWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
-
2024-05-09Full publication date if available
- Authors
-
Lisa Browning, Christine Jesus, Stefano Malacrino, Yue Guan, Kieron White, Alison Puddle, Nasullah Khalid Alham, Maryam Haghighat, Richard Colling, Jacqueline Birks, Jens Rittscher, Clare VerrillList of authors in order
- Landing page
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https://doi.org/10.3390/diagnostics14100990Publisher landing page
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https://www.mdpi.com/2075-4418/14/10/990/pdf?version=1715225498Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2075-4418/14/10/990/pdf?version=1715225498Direct OA link when available
- Concepts
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Digital pathology, Workflow, Medical physics, Quality assessment, Medicine, Prostate, Quality (philosophy), Prostate biopsy, Reliability (semiconductor), Pathology, Artificial intelligence, Computer science, External quality assessment, Internal medicine, Philosophy, Database, Physics, Quantum mechanics, Cancer, Power (physics), EpistemologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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8Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 3, 2024: 5Per-year citation counts (last 5 years)
- References (count)
-
27Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.these | 30 |
| abstract_inverted_index.tool, | 64 |
| abstract_inverted_index.total | 85 |
| abstract_inverted_index.which | 20, 50, 139, 157 |
| abstract_inverted_index.(WSI); | 40 |
| abstract_inverted_index.Whilst | 180 |
| abstract_inverted_index.action | 138 |
| abstract_inverted_index.biopsy | 106 |
| abstract_inverted_index.cases, | 155 |
| abstract_inverted_index.enough | 134 |
| abstract_inverted_index.images | 39 |
| abstract_inverted_index.impact | 22 |
| abstract_inverted_index.mainly | 130 |
| abstract_inverted_index.manual | 48 |
| abstract_inverted_index.severe | 133 |
| abstract_inverted_index.showed | 200 |
| abstract_inverted_index.tissue | 101 |
| abstract_inverted_index.types, | 196 |
| abstract_inverted_index.Digital | 0 |
| abstract_inverted_index.appears | 187 |
| abstract_inverted_index.control | 44 |
| abstract_inverted_index.current | 42, 185 |
| abstract_inverted_index.depends | 32, 46 |
| abstract_inverted_index.flagged | 116 |
| abstract_inverted_index.issues, | 129 |
| abstract_inverted_index.largely | 45 |
| abstract_inverted_index.promise | 8 |
| abstract_inverted_index.quality | 35, 43, 62, 122, 178 |
| abstract_inverted_index.setting | 81 |
| abstract_inverted_index.trained | 98 |
| abstract_inverted_index.warrant | 136 |
| abstract_inverted_index.adoption | 28 |
| abstract_inverted_index.analysed | 91 |
| abstract_inverted_index.benefit. | 201 |
| abstract_inverted_index.clinical | 25, 79, 171 |
| abstract_inverted_index.features | 19 |
| abstract_inverted_index.greatest | 188 |
| abstract_inverted_index.however, | 41 |
| abstract_inverted_index.improved | 142 |
| abstract_inverted_index.prostate | 100, 105, 192 |
| abstract_inverted_index.quality. | 144 |
| abstract_inverted_index.reliable | 148 |
| abstract_inverted_index.remedial | 137 |
| abstract_inverted_index.resulted | 140 |
| abstract_inverted_index.specimen | 195 |
| abstract_inverted_index.surgical | 153 |
| abstract_inverted_index.trained. | 162 |
| abstract_inverted_index.analysed, | 113 |
| abstract_inverted_index.automated | 60 |
| abstract_inverted_index.biopsies, | 198 |
| abstract_inverted_index.continues | 2 |
| abstract_inverted_index.developed | 57, 96 |
| abstract_inverted_index.diagnosis | 14 |
| abstract_inverted_index.digitised | 37, 170 |
| abstract_inverted_index.momentum, | 5 |
| abstract_inverted_index.pathology | 1, 80, 172 |
| abstract_inverted_index.potential | 73, 165 |
| abstract_inverted_index.prognosis | 23 |
| abstract_inverted_index.workflow, | 173 |
| abstract_inverted_index.Successful | 27 |
| abstract_inverted_index.artificial | 10 |
| abstract_inverted_index.assessment | 17, 63, 150, 190 |
| abstract_inverted_index.concordant | 127 |
| abstract_inverted_index.diagnosis. | 124 |
| abstract_inverted_index.diagnostic | 78 |
| abstract_inverted_index.previously | 56 |
| abstract_inverted_index.real-time. | 83 |
| abstract_inverted_index.specimens, | 193 |
| abstract_inverted_index.subjective | 128 |
| abstract_inverted_index.suboptimal | 121 |
| abstract_inverted_index.assessment, | 49 |
| abstract_inverted_index.feasibility | 68 |
| abstract_inverted_index.inefficient | 52 |
| abstract_inverted_index.investigate | 71 |
| abstract_inverted_index.management. | 26 |
| abstract_inverted_index.opportunity | 175 |
| abstract_inverted_index.potentially | 118 |
| abstract_inverted_index.reliability | 182 |
| abstract_inverted_index.subjective. | 54 |
| abstract_inverted_index.whole-slide | 38 |
| abstract_inverted_index.PathProfiler | 94, 145, 163 |
| abstract_inverted_index.improvement. | 179 |
| abstract_inverted_index.intelligence | 11 |
| abstract_inverted_index.non-prostate | 152 |
| abstract_inverted_index.particularly | 197 |
| abstract_inverted_index.representing | 108 |
| abstract_inverted_index.technologies | 31 |
| abstract_inverted_index.PathProfiler, | 58 |
| abstract_inverted_index.PathProfiler. | 93 |
| abstract_inverted_index.genitourinary | 88 |
| abstract_inverted_index.incorporation | 75, 167 |
| abstract_inverted_index.focus-related, | 131 |
| abstract_inverted_index.resection-type | 154 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 97 |
| corresponding_author_ids | https://openalex.org/A5013577797 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 12 |
| corresponding_institution_ids | https://openalex.org/I2802742124, https://openalex.org/I40120149 |
| citation_normalized_percentile.value | 0.94028297 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |