Placental Digitization and Diagnosis

Inclusion criteria were patients that underwent placental examination and reporting at our institution between 2011 and 2023 and had slides scanned as part of an ongoing digitization study (IRB: STU00214052). Each model used additional criteria as noted below. Placentas were examined and diagnoses rendered according to the Amsterdam criteria or precursor guidelines (27 (link)). Slides were digitized using a Leica GT450 scanner with a 40x objective magnification (0.263 microns per pixel). The 10x magnification layer was used for all studies. Pathology reports were obtained from the institutional electronic data warehouse (EDW) and processed using natural language processing (42 (link)). Patient and diagnosis information was stored in REDcap.

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Irmakci I., Nateghi R., Zhou R., Ross A.E., Yang X.J., Cooper L.A, & Goldstein J.A. (2023). Tissue contamination challenges the credibility of machine learning models in real world digital pathology. medRxiv.

Publication Preprint 2023

GT450 scanner

Diagnosis Patient Placentas

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Variable analysis

independent variables

None explicitly mentioned

dependent variables

Placental examination and reporting

control variables

Patients that underwent placental examination and reporting at the institution between 2011 and 2023
Patients who had slides scanned as part of an ongoing digitization study (IRB: STU00214052)
Placentas were examined and diagnoses rendered according to the Amsterdam criteria or precursor guidelines
Slides were digitized using a Leica GT450 scanner with a 40x objective magnification (0.263 microns per pixel)
The 10x magnification layer was used for all studies
Pathology reports were obtained from the institutional electronic data warehouse (EDW) and processed using natural language processing
Patient and diagnosis information was stored in REDcap

positive controls

None explicitly mentioned

negative controls

None explicitly mentioned

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