Diagnostic accuracy of an automated microscope solution (miLab™) in detecting malaria parasites in symptomatic patients at point-of-care in Sudan: a case-control study.

BACKGROUND: Microscopic detection of malaria parasites is labour-intensive, time-consuming, and expertise-demanding. Moreover, the slide interpretation is highly dependent on the staining technique and the technician's expertise. Therefore, there is a growing interest in next-generation, fully- or semi-integrated microscopes that can improve slide preparation and examination. This study aimed to evaluate the clinical performance of miLab™ (Noul Inc., Republic of Korea), a fully-integrated automated microscopy device for the detection of malaria parasites in symptomatic patients at point-of-care in Sudan. METHODS: This was a prospective, case-control diagnostic accuracy study conducted in primary health care facilities in rural Khartoum, Sudan in 2020. According to the outcomes of routine on-site microscopy testing, 100 malaria-positive and 90 malaria-negative patients who presented at the health facility and were 5 years of age or older were enrolled consecutively. All consenting patients underwent miLab™ testing and received a negative or suspected result. For the primary analysis, the suspected results were regarded as positive (automated mode). For the secondary analysis, the operator reviewed the suspected results and categorized them as either negative or positive (corrected mode). Nested polymerase chain reaction (PCR) was used as the reference standard, and expert light microscopy as the comparator. RESULTS: Out of the 190 patients, malaria diagnosis was confirmed by PCR in 112 and excluded in 78. The sensitivity of miLab™ was 91.1% (95% confidence interval [CI] 84.2-95.6%) and the specificity was 66.7% (95% Cl 55.1-67.7%) in the automated mode. The specificity increased to 96.2% (95% Cl 89.6-99.2%), with operator intervention in the corrected mode. Concordance of miLab with expert microscopy was substantial (kappa 0.65 [95% CI 0.54-0.76]) in the automated mode, but almost perfect (kappa 0.97 [95% CI 0.95-0.99]) in the corrected mode. A mean difference of 0.359 was found in the Bland-Altman analysis of the agreement between expert microscopy and miLab™ for quantifying parasite counts. CONCLUSION: When used in a clinical context, miLab™ demonstrated high sensitivity but low specificity. Expert intervention was shown to be required to improve the device's specificity in its current version. miLab™ in the corrected mode performed similar to expert microscopy. Before clinical application, more refinement is needed to ensure full workflow automation and eliminate human intervention. Trial registration ClinicalTrials.gov: NCT04558515.

DNA Karyometry for Automated Detection of Cancer Cells.

Background: Microscopical screening of cytological samples for the presence of cancer cells at high throughput with sufficient diagnostic accuracy requires highly specialized personnel which is not available in most countries. Methods: Using commercially available automated microscope-based screeners (MotiCyte and EasyScan), software was developed which is able to classify Feulgen-stained nuclei into eight diagnostically relevant types, using supervised machine learning. the nuclei belonging to normal cells were used for internal calibration of the nuclear DNA content while nuclei belonging to those suspicious of being malignant were specifically identified. The percentage of morphologically abnormal nuclei was used to identify samples suspected of malignancy, and the proof of DNA-aneuploidy was used to definitely determine the state malignancy. A blinded study was performed using oral smears from 92 patients with Fanconi anemia, revealing oral leukoplakias or erythroplakias. In an earlier study, we compared diagnostic accuracies on 121 serous effusion specimens. In addition, using a blinded study employing 80 patients with prostate cancer who were under active surveillance, we aimed to identify those whose cancers would not advance within 4 years. Results: Applying a threshold of the presence of >4% of morphologically abnormal nuclei from oral squamous cells and DNA single-cell or stemline aneuploidy to identify samples suspected of malignancy, an overall diagnostic accuracy of 91.3% was found as compared with 75.0% accuracy determined by conventional subjective cytological assessment using the same slides. Accuracy of automated screening effusions was 84.3% as compared to 95.9% of conventional cytology. No prostate cancer patients under active surveillance, revealing DNA-grade 1, showed progress of their disease within 4.1 years. Conclusions: An automated microscope-based screener was developed which is able to identify malignant cells in different types of human specimens with a diagnostic accuracy comparable with subjective cytological assessment. Early prostate cancers which do not progress despite applying any therapy could be identified using this automated approach.

A review on low-cost microscopes for Open Science.

This article presents a review after an exhaustive search that yielded 23 works carried out in the last decade for the availability of optical microscopes with open hardware as a low-cost alternative to commercial systems. These works were developed with the aim of covering needs within several areas such as: Bio Sciences research in institutions with limited resources, diagnosis of diseases and health screenings in large populations in developing countries, and training in educational contexts with a need for high availability of equipment and low replacement cost. The analysis of the selected works allows us to classify the analyzed solutions into two main categories, for which their essential characteristics are enumerated: portable field microscopes and multipurpose automated microscopes. Moreover, this work includes a discussion on the degree of maturity of the solutions in terms of the adoption of practices aligned with the development of Open Science. RESEARCH HIGHLIGHTS: Concise review on low-cost microscopes for developing Open Science, exposing the role of smartphone-based microscopy. The work classifies microscopes in two main categories: (1) portable field microscopes, and (2) multipurpose automated microscopes.

Novel TB smear microscopy automation system in detecting acid-fast bacilli for tuberculosis - A multi-center double blind study.

Due to COVID-19 pandemic, there is a large global drop in the number of newly diagnosed cases with tuberculosis (TB) worldwide. Actions to mitigate and reverse the impact of the COVID-19 pandemic on TB are urgently needed. Recent development of TB smear microscopy automation systems using artificial intelligence may increase the sensitivity of TB smear microscopy. The objective is to evaluate the performance of an automation system (µ-Scan 2.0, Wellgen Medical) over manual smear microscopy in a multi-center, double-blind trial. Total of 1726 smears were enrolled. Referee medical technician and culture served as primary and secondary gold standards for result discrepancy. Results showed that, compared to manual microscopy, the µ-Scan 2.0's performance of accuracy, sensitivity and specificity were 95.7% (1651/1726), 87.7% (57/65), and 96.0% (1594/1661), respectively. The negative predictive value was 97.8% at prevalence of 8.2%. Manual smear microscopy remains the primary diagnosis of pulmonary tuberculosis (TB). Use of automation system could achieve higher TB smear sensitivity and laboratory efficiency. It can also serve as a screening tool that complements molecular methods to reduce the total cost for TB diagnosis and control. Furthermore, such automation system is capable of remote access by internet connection and can be deployed in area with limited medical resources.

The design and application of an automated microscope developed based on deep learning for fungal detection in dermatology.

BACKGROUND: Light microscopy to study the infection of fungi in skin specimens is time-consuming and requires automation. OBJECTIVE: We aimed to design and explore the application of an automated microscope for fungal detection in skin specimens. METHODS: An automated microscope was designed, and a deep learning model was selected. Skin, nail and hair samples were collected. The sensitivity and the specificity of the automated microscope for fungal detection were calculated by taking the results of human inspectors as the gold standard. RESULTS: An automated microscope was built, and an image processing model based on the ResNet-50 was trained. A total of 292 samples were collected including 236 skin samples, 50 nail samples and six hair samples. The sensitivities of the automated microscope for fungal detection in skin, nails and hair were 99.5%, 95.2% and 60%, respectively, and the specificities were 91.4%, 100% and 100%, respectively. CONCLUSION: The automated microscope we developed is as skilful as human inspectors for fungal detection in skin and nail samples; however, its performance in hair samples needs to be improved.

Inexpensive robotic system for standard and fluorescent imaging of protein crystals.

Protein-crystallization imaging and classification is a labor-intensive process typically performed either by humans or by instruments that currently cost well over $100 000. This cost puts the use of crystallization-trial imaging outside the reach of most academic laboratories, and also start-up biotechnology firms, where resources are scarce. An imaging system has been designed and prototyped which automatically captures images from multi-well protein-crystallization experiments using both standard and fluorescent imaging techniques at a cost 28 times lower than current market rates. The machine uses a Panowin F1 3D printer as a base and controls it using G-code commands sent from a Python script running on a desktop computer. A graphical user interface (GUI) was developed to enable users to control the machine and facilitate image capture, classification and editing. A 488 nm laser diode and a 525 nm filter were incorporated to allow in situ fluorescent imaging of proteins trace-labeled with a fluorophore, Alexa Fluor 488. The instrument was primarily designed using a 3D printer and augmented using commercially available parts, and this publication aims to serve as a guide for comparable in-laboratory robotics projects.

An inexpensive system for imaging the contents of multi-well plates.

An inexpensive system for automated imaging of the contents of 12-, 24- and 96-well plates has been built. The xyz stage is constructed from parts from a light-duty computer numerical control wood-carving/engraving machine, and the Arduino-based board was wired so that it can trigger still images or movies though a microscope-mounted digital camera. The translation stage provides reproducible three-dimensional movement of the sample over a volume of 160 mm in x, 100 mm in y and 40 mm in z. A Python script generates the G-code command file that scans the plate and collects a series of z-stacked images of each sample. A second Python script automates the calculation of images with a digitally enhanced depth of field. The imaging system is currently being used to facilitate screening for protein crystals, but it could be used to automate the imaging of many other types of samples in multi-well plates.