Morris, Heather

Abstract Effective filtration and concentration of stool specimens is an essential pre-analytical step for reducing fecal debris and improving organism recovery using microscopy-based ova and parasite (O&P) examination. This study evaluated three commercially available fecal sedimentation-based filtration/concentration systems – ParaPak SpinCon® (Meridian Bioscience), Mini Parasep SF® (Apacor), and the newly-available Paradevice® (Reingenuity) – for qualitative parasite detection and workflow logistics using conventional and artificial intelligence (AI)-assisted microscopy. Forty clinical stool specimens (20 parasite-positive and 20 parasite-negative) were processed with the 3 devices, and the resultant 120 wet mount and 120 trichrome-stained smear preparations were examined using conventional microscopy. Trichrome-stained slides were also scanned at 40x magnification using a Hamamatsu® NanoZoomerS360® flatbed digital slide scanner and images were analyzed using the Techcyte® Fusion Human Fecal Trichrome AI algorithm. Positive and indeterminate digital findings were confirmed by conventional glass slide microscopy. Slides and digital images were reviewed in a blinded manner. Concordance was assessed among the 360 initial evaluations (microscopy and AI-assisted), and discrepant parasitology results were resolved through re-review and specimen reprocessing as needed. Final qualitative agreement across slide/image evaluations using all three concentration systems was 100%. Minor discrepancies in protozoan and white/red blood cell detection/identification were noted in 5 and 7 cases, respectively, and likely reflected sampling and observer variability. While the three concentration systems produced equivalent qualitative results, the Paradevice® and Mini Parasep SF® offered the most streamlined workflows. These findings support the Paradevice® and Mini Parasep SF® as efficient, analytically equivalent systems that are compatible with traditional and AI-assisted O&P workflows. Importance Microscopy-based examination of stool specimens remains an important diagnostic tool for intestinal parasitic infections. Filtration and concentration methods improve test performance by increasing organism recovery and reducing obscuring fecal debris prior to evaluation. These methods may also facilitate the preparation of thin monolayer trichrome-stained slides for digital scanning and automated parasite detection. Because many clinical laboratories rely on commercial filtration and concentration systems, supply chain disruptions, product shortages, and manufacturer recalls threaten testing continuity, underscoring the need for validated alternatives. This study demonstrates that multiple commercially available concentration systems provide equivalent parasite detection while supporting both conventional microscopy and emerging digital workflows. These findings provide laboratories with evidence-based alternatives that enhance operational resilience without compromising diagnostic performance.

Abstract Objectives Recent work has demonstrated that automated fluorescence flow cytometry (FLC) is a potential alternative for the detection and quantification of Plasmodium parasites. The objective of this study was to apply this novel FLC method to detect and quantify Babesia parasites in venous blood and compare results to light microscopy and polymerase chain reaction methods. Methods An automated hematology/malaria analyzer (XN-31; Sysmex) was used to detect and quantify B microti–infected red blood cells from residual venous blood samples (n = 250: Babesia positive, n = 170; Babesia negative, n = 80). As no instrument software currently exists for Babesia, qualitative and quantitative machine learning (ML) algorithms were developed to facilitate analysis. Results Performance of the ML models was verified against the XN-31 software using P falciparum–infected samples. When applied to Babesia-infected samples, the qualitative ML model demonstrated an area under the curve (AUC) of 0.956 (sensitivity, 95.9%; specificity, 83.3%) relative to polymerase chain reaction. For valid scattergrams, the qualitive model achieved an AUC of 1.0 (sensitivity and specificity, 100%), while the quantitative model demonstrated an AUC of 0.986 (sensitivity, 94.4%; specificity, 100%). Conclusions This investigation demonstrates that Babesia parasites can be detected and quantified directly from venous blood using FLC. Although promising, opportunities remain to improve the general applicability of the method.

Abstract Background Deep convolutional neural networks (DNN) have been successfully applied to the detection and segmentation of objects in gigapixel digital slides. In this study, trained laboratory technologists utilized a DNN-assisted digital workflow (Techcyte, Inc.; Orem, UT) to evaluate clinical stool specimens for the presence and identification of protozoan parasites. Methods Slides were prepared from 142 stool specimens by spreading a thin layer of concentrated stool onto a slide, staining with modified-trichrome, and coverslipping with permanent mounting medium. Slides were then scanned using a NanoZoomer 360 digital slide scanner (Hamamatsu Photonics K.K., Japan) at 40x apparent magnification and prospectively analyzed by the Techcyte DNN. Objects of interest were identified and classified by the DNN (protozoa, leukocytes, erythrocytes) and presented to a laboratory technologist for interpretation. (See example image below.) DNN-assisted results were compared to those from conventional microscopic examination. To determine analytical sensitivity, slides were prepared from serial, 2-fold dilutions of 3 unique stool specimens (6 slides at each dilution/specimen) and analyzed by the DNN-assisted method and conventional microscopy. Example of Techcyte DNN analysis review screen Objects of interest most likely to be significant are presented to the technologist for review in an intuitive web-based format. Parasites are confirmed as present or absent by a trained technologist after reviewing these images. Results Digital analysis was highly reproducible, showing 100% slide level agreement (positive/negative) within and between runs using positive and negative slides. There was 98.7% and 98.3% positive and negative agreement, respectively, between conventional and DNN-assisted results. Of the 142 slides conventional microscopy detected parasites in 82, while the DNN-assisted method detected 83. The DNN-assisted method also detected more slides with leukocytes (29 vs 24) and erythrocytes (12 vs 8). All discrepant results were confirmed by repeat conventional microscopy. Analytical sensitivity of the DNN-assisted method was equivalent or superior to conventional microscopy review, detecting down to 1:64 dilutions in 6/6 slides per specimen (conventional = 1:32 to 1:64), extending for some slides to detection at dilutions of 1:2048. Conclusion The DNN-assisted method facilitates rapid screening of negative specimens, while providing sensitive and accurate detection and classification of protozoa. Disclosures All Authors: No reported disclosures