Artificial Intelligence-Based Quality Assessment of Histopathology Whole-Slide Images within a Clinical Workflow: Assessment of ‘PathProfiler’ in a Diagnostic Pathology Setting
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
Affiliations
Lisa Browning
Department of Cellular Pathology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
Christine Jesus
Department of Cellular Pathology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
Stefano Malacrino
Nuffield Department of Surgical Sciences, University of Oxford, Oxford OX3 9DU, UK
Yue Guan
Department of Cellular Pathology, Royal Berkshire Hospital, Royal Berkshire NHS Foundation Trust, Reading RG1 5AN, UK
Kieron White
Department of Cellular Pathology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
Alison Puddle
Department of Cellular Pathology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
Nasullah Khalid Alham
Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK
Maryam Haghighat
School of Electrical Engineering and Robotics, Queensland University of Technology, Brisbane, QLD 4000, Australia
Richard Colling
Department of Cellular Pathology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
Jacqueline Birks
Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK
Jens Rittscher
Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK
Clare Verrill
Department of Cellular Pathology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
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.
