Digital pathology workflows can improve pathology operations by allowing reliable and fast retrieval of digital images, digitally reviewing pathology slides, enabling remote work and telepathology, use of computer-aided tools, and sharing of digital images for research and educational purposes. The need for quality systems is a prerequisite for successful clinical-grade digital pathology adoption and patient safety. In this article, we describe the development of a structured digital pathology laboratory quality management system (QMS) for clinical digital pathology operations at Memorial Sloan Kettering Cancer Center (MSK). This digital pathology-specific QMS development stemmed from the gaps that were identified when MSK integrated digital pathology into its clinical practice. The digital scan team in conjunction with the Department of Pathology and Laboratory Medicine quality team developed a QMS tailored to the scanning operation to support departmental and institutional needs. As a first step, systemic mapping of the digital pathology operations identified the prescan, scan, and postscan processes; instrumentation; and staffing involved in the digital pathology operation. Next, gaps identified in quality control and quality assurance measures led to the development of standard operating procedures and training material for the different roles and workflows in the process. All digital pathology-related documents were subject to regulatory review and approval by departmental leadership. The quality essentials were developed into an extensive Digital Pathology Quality Essentials framework to specifically address the needs of the growing clinical use of digital pathology technologies. Using the unique digital experience gained at MSK, we present our recommendations for QMS for large-scale digital pathology operations in clinical settings.
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http://dx.doi.org/10.1016/j.labinv.2023.100246 | DOI Listing |
EClinicalMedicine
December 2024
Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Background: Infant alertness and neurologic changes can reflect life-threatening pathology but are assessed by physical exam, which can be intermittent and subjective. Reliable, continuous methods are needed. We hypothesized that our computer vision method to track movement, pose artificial intelligence (AI), could predict neurologic changes in the neonatal intensive care unit (NICU).
View Article and Find Full Text PDFPeerJ
January 2025
State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, China.
Objective: The study aims to develop a diagnostic model using intraoral photographs to accurately detect and classify early detection of enamel demineralization on tooth surfaces.
Methods: A retrospective analysis was conducted with 208 patients aged 14 to 44. A total of 624 high-quality digital images captured under standardized conditions were used to construct a deep learning model based on the Mask region-based convolutional neural network (Mask R-CNN).
BJU Int
January 2025
Division of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia.
Objective: To perform a systematic review and meta-analysis to assess the relationship between intraprostatic maximum standardised uptake value (SUV) of the dominant prostatic lesion as measured on preoperative prostate-specific membrane antigen (PSMA) positron emission tomography (PET) with radical prostatectomy International Society of Urological Pathology (ISUP) Grade Group, pathological tumour (pT) staging, and biochemical recurrence (BCR).
Methods: Prostate-specific membrane antigen PET may offer non-invasive assessment of histopathological and oncological outcomes before definitive treatment. SUV of the dominant lesion has been explored as a prognostic biomarker.
JBI Evid Implement
January 2025
Queensland Digital Health Centre, Centre for Health Services Research, Faculty of Medicine, The University of Queensland, Brisbane, Qld, Australia.
Abstract: Inpatient diabetes management presents a complex challenge that is distinct from outpatient management. This is due to acute changes in physiology, medication regimens, and eating patterns associated with hospitalization, alongside the condition's prevalent and variable nature. The conventional systems for managing glycemic control in hospital have been found lacking, with gaps in data integration, decision support, and timely intervention.
View Article and Find Full Text PDFVirchows Arch
January 2025
Belgian Society of Pathology, Brussels, Belgium.
The adoption of Standardized Structured Reporting (SSR) in pathology offers significant potential to improve data consistency, completeness, and interoperability. This study combines quantitative data from an online survey of Belgian pathologists with qualitative insights from focus group interviews to identify key factors influencing SSR implementation. Survey results demonstrate strong support for SSR, particularly in enhancing report uniformity, completeness, and efficiency, especially in multidisciplinary teams and for secondary data use.
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