Hutchinson, J Ciaran;
Picarsic, Jennifer;
McGenity, Clare;
Treanor, Darren;
Williams, Bethany;
Sebire, Neil J;
(2024)
Whole Slide Imaging, Artificial Intelligence, and Machine Learning in Pediatric and Perinatal Pathology: Current Status and Future Directions.
Pediatric and Developmental Pathology
10.1177/10935266241299073.
(In press).
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Abstract
The integration of artificial intelligence (AI) into healthcare is becoming increasingly mainstream. Leveraging digital technologies, such as AI and deep learning, impacts researchers, clinicians, and industry due to promising performance and clinical potential. Digital pathology is now a proven technology, enabling generation of high-resolution digital images from glass slides (whole slide images; WSI). WSIs facilitates AI-based image analysis to aid pathologists in diagnostic tasks, improve workflow efficiency, and address workforce shortages. Example applications include tumor segmentation, disease classification, detection, quantitation and grading, rare object identification, and outcome prediction. While advancements have occurred, integration of WSI-AI into clinical laboratories faces challenges, including concerns regarding evidence quality, regulatory adaptations, clinical evaluation, and safety considerations. In pediatric and developmental histopathology, adoption of AI could improve diagnostic efficiency, automate routine tasks, and address specific diagnostic challenges unique to the specialty, such as standardizing placental pathology and developmental autopsy findings, as well as mitigating staffing shortages in the subspeciality. Additionally, AI-based tools have potential to mitigate medicolegal implications by enhancing reproducibility and objectivity in diagnostic evaluations. An overview of recent developments and challenges in applying AI to pediatric and developmental pathology, focusing on machine learning methods applied to WSIs of pediatric pathology specimens is presented.
| Type: | Article |
|---|---|
| Title: | Whole Slide Imaging, Artificial Intelligence, and Machine Learning in Pediatric and Perinatal Pathology: Current Status and Future Directions |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1177/10935266241299073 |
| Publisher version: | https://doi.org/10.1177/10935266241299073 |
| Language: | English |
| Additional information: | This version is the author-accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | artificial intelligence, digital pathology, machine learning, whole slide imaging |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health > Population, Policy and Practice Dept |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10202372 |
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