Magee, Kyle

Abstract Background Gram stain (GS) is essential for preliminary bacterial identification. The lack of a standardized quality control method for GS is a significant drawback. By utilizing a deep convolutional neural network (CNN) design, we present a proof-of-concept for a novel solution to GS quality assurance. Using this approach, current manual GS, and potentially automated GS methods, may possess a standardized method of quality control. Methods Twenty-nine GS from positive blood cultures encompassing the spectrum of GS quality (acceptable, over/under decolorization, smear thickness, stain artifact) were selected by two expert microbiologists. A total of 197 images were obtained using a Cognex D-900 camera and Olympus BX-43 microscope at 1000X magnification. Images were classified using deep learning software (Cognex, ViDi Suite 4.1). Eighty-one images were classified as suboptimal and 116 were classified as optimal by expert review. To address background variation due to manual GS, we implemented a screening classification tool to separate images into 5 distinct classes (pale, rose, orange, indigo, variegated). The training and test sets consisted of 100 and 97 images respectively. Using a chaining technique, a second classification tool was added after the screening classification tool. The second tool further divided the 197 images into two dataset classes: optimal and suboptimal. The second tool used 105 images for training and 92 images for the test set. Model performance was compared to expert microbiology review, with GS verified by organism identification using MALDI-TOF. Results Cognex ViDi Suite 4.1 could discriminate between optimal and suboptimal GS images. The chained classification tool was able to be trained with 100% recall and 100% accuracy, with a resulting F-score of 1.00. Figure 1 VisionPro GUI with optimal image (MRSA) Conclusion Deep learning enabled image analysis is an effective method for sorting GS which are acceptable for further interpretation and suboptimal smears which require rework. Additional study is warranted to develop deep learning image analysis tools for the interpretation of GS on multiple specimen types and incorporate into automated GS systems. Disclosures Jane Hata, PhD , D(ABMM), Roche Molecular Systems: Grant/Research Support Kyle Magee, MLS(ASCP), SeluxDx: Employee

Nocardia spp. is a Gram-positive, partially acid-fast aerobic bacterium usually associated with infection in immunocompromised people. The most common sites of infection are the skin, lungs, and the brain, however disease can disseminate and affect every organ. Clinical manifestations of cutaneous disease are varied and frequently misdiagnosed. We present a case of an immunocompetent 66-year-old man who sustained a left finger injury while gardening. He was misdiagnosed on several occasions and treated with inappropriate antibiotics against Streptococcus spp. and Staphylococcus spp. When infection spread cutaneously, sporotrichoid (lymphocutaneous) nocardiosis was suspected and the patient was started on appropriate therapy with Bactrim which resulted in a cure. We also summarize the literature on lymphocutaneous infection by Nocardia brasiliensis. By reporting this case, we want to raise awareness among clinicians about unusual causes of cellulitis, the differential diagnosis of lymphocutaneous infection and the importance of obtaining a detailed exposure history to assist in the prompt diagnosis of nocardiosis.