Publications

We maintain this section to inform interested users about independent scientific studies conducted on MetaSystems products. We assume no responsibility or liability regarding the accuracy or correct use of the information or statements provided by external authors. The conclusions or statements expressed in the publications listed are those of the external authors or researchers. The publications may involve user-specific adaptations of MetaSystems products. They are not intended for diagnostic use. For publications covered by the Intended Purpose of Metafer or Ikaros, please refer to the respective instructions for use (IFU).

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The British journal of dermatology
January, 2021

COVID-19 related dermatosis in November 2019. Could this case be Italy's patient zero?

Gianotti, R., Barberis, M., Fellegara, G., Galván-Casas, C., Gianotti, E.

<p>Milan, the largest city in northern Italy, was one of the first European metropolitan areas to be affected by the COVID-19 pandemic. We analyzed skin biopsies of patients from Milan with dermatoses and positive PCR swabs for SARS-CoV-2 at different stages of the infection (1,2). The results were compared to skin biopsies of 20 COVID-19 non-diagnosed patients with dermatoses, who were at high-risk of COVID-19 infection.</p>

Digital object identifier (DOI): 10.1111/bjd.19804

Tuberculosis (Edinburgh, Scotland), 125, 101993
September, 2020

Machine-assisted interpretation of auramine stains substantially increases through-put and sensitivity of microscopic tuberculosis diagnosis.

Horvath, L., Hänselmann, S., Mannsperger, H., Degenhardt, S., Last, K., Zimmermann, S., Burckhardt, I.

Of all bacterial infectious diseases, infection by Mycobacterium tuberculosis poses one of the highest morbidity and mortality burdens on humans throughout the world. Due to its speed and cost-efficiency, manual microscopy of auramine-stained sputum smears remains a crucial first-line detection method. However, it puts considerable workload on laboratory staff and suffers from a limited sensitivity. Here we validate a scanning and analysis system that combines fully-automated microscopy with deep-learning based image analysis. After automated scanning, the system summarizes diagnosis-relevant image information and presents it to the microbiologist in order to assist diagnosis. We tested the benefit of the automated scanning and analysis system using 531 slides from routine workflow, of which 56 were from culture positive specimen. Assistance by the scanning and analysis system allowed for a higher sensitivity (40/56 positive slides detected) than manual microscopy (34/56 positive slides detected), while greatly reducing manual slide-analysis time from a recommended 5-15 min to around 10 s per slide on average.

Digital object identifier (DOI): 10.1016/j.tube.2020.101993