DUBLINE: A Deep Unfolding Network for B-line Detection in Lung Ultrasound Images

In the context of lung ultrasound, the detection of B-lines, which are indicative of interstitial lung disease and pulmonary edema, plays a pivotal role in clinical diagnosis. Current methods still rely on visual inspection by experts. Vision-based automatic B-line detection methods have been developed, but their performance has yet to improve in terms of both accuracy and computational speed. This paper presents a novel approach to posing B-line detection as an inverse problem via deep unfolding of the Alternating Direction Method of Multipliers (ADMM). It tackles the challenges of data labelling and model training in lung ultrasound image analysis by harnessing the capabilities of deep neural networks and model-based methods. Our objective is to substantially enhance diagnostic accuracy while ensuring efficient real-time capabilities. The results show that the proposed method runs more than 90 times faster than the traditional model-based method and achieves an F1 score that is 10.6% higher.Comment: 4 pages, 3 figures, conferenc

Deep learning enhanced mobile-phone microscopy

Mobile-phones have facilitated the creation of field-portable, cost-effective imaging and sensing technologies that approach laboratory-grade instrument performance. However, the optical imaging interfaces of mobile-phones are not designed for microscopy and produce spatial and spectral distortions in imaging microscopic specimens. Here, we report on the use of deep learning to correct such distortions introduced by mobile-phone-based microscopes, facilitating the production of high-resolution, denoised and colour-corrected images, matching the performance of benchtop microscopes with high-end objective lenses, also extending their limited depth-of-field. After training a convolutional neural network, we successfully imaged various samples, including blood smears, histopathology tissue sections, and parasites, where the recorded images were highly compressed to ease storage and transmission for telemedicine applications. This method is applicable to other low-cost, aberrated imaging systems, and could offer alternatives for costly and bulky microscopes, while also providing a framework for standardization of optical images for clinical and biomedical applications

Early-detection and classification of live bacteria using time-lapse coherent imaging and deep learning

We present a computational live bacteria detection system that periodically captures coherent microscopy images of bacterial growth inside a 60 mm diameter agar-plate and analyzes these time-lapsed holograms using deep neural networks for rapid detection of bacterial growth and classification of the corresponding species. The performance of our system was demonstrated by rapid detection of Escherichia coli and total coliform bacteria (i.e., Klebsiella aerogenes and Klebsiella pneumoniae subsp. pneumoniae) in water samples. These results were confirmed against gold-standard culture-based results, shortening the detection time of bacterial growth by >12 h as compared to the Environmental Protection Agency (EPA)-approved analytical methods. Our experiments further confirmed that this method successfully detects 90% of bacterial colonies within 7-10 h (and >95% within 12 h) with a precision of 99.2-100%, and correctly identifies their species in 7.6-12 h with 80% accuracy. Using pre-incubation of samples in growth media, our system achieved a limit of detection (LOD) of ~1 colony forming unit (CFU)/L within 9 h of total test time. This computational bacteria detection and classification platform is highly cost-effective (~$0.6 per test) and high-throughput with a scanning speed of 24 cm2/min over the entire plate surface, making it highly suitable for integration with the existing analytical methods currently used for bacteria detection on agar plates. Powered by deep learning, this automated and cost-effective live bacteria detection platform can be transformative for a wide range of applications in microbiology by significantly reducing the detection time, also automating the identification of colonies, without labeling or the need for an expert.Comment: 24 pages, 6 figure

Computational cytometer based on magnetically modulated coherent imaging and deep learning.

Detecting rare cells within blood has numerous applications in disease diagnostics. Existing rare cell detection techniques are typically hindered by their high cost and low throughput. Here, we present a computational cytometer based on magnetically modulated lensless speckle imaging, which introduces oscillatory motion to the magnetic-bead-conjugated rare cells of interest through a periodic magnetic force and uses lensless time-resolved holographic speckle imaging to rapidly detect the target cells in three dimensions (3D). In addition to using cell-specific antibodies to magnetically label target cells, detection specificity is further enhanced through a deep-learning-based classifier that is based on a densely connected pseudo-3D convolutional neural network (P3D CNN), which automatically detects rare cells of interest based on their spatio-temporal features under a controlled magnetic force. To demonstrate the performance of this technique, we built a high-throughput, compact and cost-effective prototype for detecting MCF7 cancer cells spiked in whole blood samples. Through serial dilution experiments, we quantified the limit of detection (LoD) as 10 cells per millilitre of whole blood, which could be further improved through multiplexing parallel imaging channels within the same instrument. This compact, cost-effective and high-throughput computational cytometer can potentially be used for rare cell detection and quantification in bodily fluids for a variety of biomedical applications

Identification of pathogenic bacteria in complex samples using a smartphone based fluorescence microscope

Diagnostics based on fluorescence imaging of biomolecules is typically performed in well-equipped laboratories and is in general not suitable for remote and resource limited settings. Here we demonstrate the development of a compact, lightweight and cost-effective smartphone-based fluorescence microscope, capable of detecting signals from fluorescently labeled bacteria. By optimizing a peptide nucleic acid (PNA) based fluorescence in situ hybridization (FISH) assay, we demonstrate the use of the smartphone-based microscope for rapid identification of pathogenic bacteria. We evaluated the use of both a general nucleic acid stain as well as species-specific PNA probes and demonstrated that the mobile platform can detect bacteria with a sensitivity comparable to that of a conventional fluorescence microscope. The PNA-based FISH assay, in combination with the smartphone-based fluorescence microscope, allowed us to qualitatively analyze pathogenic bacteria in contaminated powdered infant formula (PIF) at initial concentrations prior to cultivation as low as 10 CFU per 30 g of PIF. Importantly, the detection can be done directly on the smartphone screen, without the need for additional image analysis. The assay should be straightforward to adapt for bacterial identification also in clinical samples. The cost-effectiveness, field-portability and simplicity of this platform will create various opportunities for its use in resource limited settings and point-of-care offices, opening up a myriad of additional applications based on other fluorescence-based diagnostic assays

Metisilin dirençli Staphylococcus aureus ve vankomisin dirençli Enterococcus suşlarının tanısı için MEMS tabanlı elektrokimyasal sensör.

Methicillin Resistant Staphylococcus aureus (MRSA) is one of the most important threats of nosocomial infections in many regions of the world and Vancomycin Resistant Enterococcus (VRE) is an emerging pathogen that develops full resistance against third-generation glycopeptide antibiotics. Conventional methods for identification of MRSA and VRE generally depend on culturing, which requires incubation of biological samples at least 24-72 hours to get accurate results. These methods are time consuming and necessitate optical devices and experts for evaluation of the results. On the other hand, early diagnosis and initiation of appropriate treatment are necessary to decrease morbidity and mortality rates. Thus, new diagnostic systems are essential for rapid and accurate detection of biological analytes at the point of care. This study presents design, fabrication, and implementation of MEMS based micro electrochemical sensor (µECS) to detect the methicillin resistance in Staphylococcus aureus and vancomycin resistance in Enterococcus species. To the best of our knowledge, the developed sensor is the first µECS which utilizes on-chip reference (Ag), working (Au), and counter (Pt) electrodes together with a microchannel to detect MRSA and VRE. The characterization of the designed sensor was achieved analyzing the interactions of the buffer solutions and solvents with the electrodes and Parylene C film layer by using optical and electrochemical methods. Specific parts of genes that are indicators of antimicrobial resistances were used in order to detect the resistances with high selectivity and sensitivity. Thus, synthetic DNA and bacterial PCR product were used as target probes in redox marker based detection and enzyme based detection, respectively. In order to enhance the hybridization, folding structures of the capture probe were investigated by using mfold Web Server. In redox marker based detection, the hybridization of DNA was indirectly detected by using Hoechst 33258 as redox marker with differential pulse voltammetry. The cross reactivity of the tests were performed by using different target probes of femA genes of S. aureus and S. epidermis, which are the major genes detected in methicillin detection assays. Consequently, amplification of signal by using horseradish peroxidase and TMB/H2O2 as substrate was achieved in order to enhance detection sensitivity. The sensor could detect 0.01 nM 23-mer specific part of mecA gene with redox marker based detection and 10 times diluted PCR product with enzyme-based detection in about six hours including the steps of sample preparation from whole blood. This sensor with its compatibility to MEMS fabrication processes and IC technology has a promising potential for a hand-held device for POC through the integration of micropotentiostatPh.D. - Doctoral Progra

Mobile Diagnostic Devices for Digital Transformation in Personalized Healthcare

Mobile devices have increasingly become an essential part of the healthcare system worldwide [...