6 research outputs found
Highly sensitive quantitative phase microscopy and deep learning aided with whole genome sequencing for rapid detection of infection and antimicrobial resistance
Current state-of-the-art infection and antimicrobial resistance (AMR) diagnostics are based on culture-based methods with a detection time of 48–96 h. Therefore, it is essential to develop novel methods that can do real-time diagnoses. Here, we demonstrate that the complimentary use of label-free optical assay with whole-genome sequencing (WGS) can enable rapid diagnosis of infection and AMR. Our assay is based on microscopy methods exploiting label-free, highly sensitive quantitative phase microscopy (QPM) followed by deep convolutional neural networks-based classification. The workflow was benchmarked on 21 clinical isolates from four WHO priority pathogens that were antibiotic susceptibility tested, and their AMR profile was determined by WGS. The proposed optical assay was in good agreement with the WGS characterization. Accurate classification based on the gram staining (100% recall for gram-negative and 83.4% for gram-positive), species (98.6%), and resistant/susceptible type (96.4%), as well as at the individual strain level (100% sensitivity in predicting 19 out of the 21 strains, with an overall accuracy of 95.45%). The results from this initial proof-of-concept study demonstrate the potential of the QPM assay as a rapid and first-stage tool for species, strain-level classification, and the presence or absence of AMR, which WGS can follow up for confirmation. Overall, a combined workflow with QPM and WGS complemented with deep learning data analyses could, in the future, be transformative for detecting and identifying pathogens and characterization of the AMR profile and antibiotic susceptibility
Highly sensitive quantitative phase microscopy and deep learning aided with whole genome sequencing for rapid detection of infection and antimicrobial resistance
Current state-of-the-art infection and antimicrobial resistance (AMR) diagnostics are based on culture-based methods with a detection time of 48–96 h. Therefore, it is essential to develop novel methods that can do real-time diagnoses. Here, we demonstrate that the complimentary use of label-free optical assay with whole-genome sequencing (WGS) can enable rapid diagnosis of infection and AMR. Our assay is based on microscopy methods exploiting label-free, highly sensitive quantitative phase microscopy (QPM) followed by deep convolutional neural networks-based classification. The workflow was benchmarked on 21 clinical isolates from four WHO priority pathogens that were antibiotic susceptibility tested, and their AMR profile was determined by WGS. The proposed optical assay was in good agreement with the WGS characterization. Accurate classification based on the gram staining (100% recall for gram-negative and 83.4% for gram-positive), species (98.6%), and resistant/susceptible type (96.4%), as well as at the individual strain level (100% sensitivity in predicting 19 out of the 21 strains, with an overall accuracy of 95.45%). The results from this initial proof-of-concept study demonstrate the potential of the QPM assay as a rapid and first-stage tool for species, strain-level classification, and the presence or absence of AMR, which WGS can follow up for confirmation. Overall, a combined workflow with QPM and WGS complemented with deep learning data analyses could, in the future, be transformative for detecting and identifying pathogens and characterization of the AMR profile and antibiotic susceptibility
From Hours to Seconds: Towards 100x Faster Quantitative Phase Imaging via Differentiable Microscopy
With applications ranging from metabolomics to histopathology, quantitative
phase microscopy (QPM) is a powerful label-free imaging modality. Despite
significant advances in fast multiplexed imaging sensors and
deep-learning-based inverse solvers, the throughput of QPM is currently limited
by the speed of electronic hardware. Complementarily, to improve throughput
further, here we propose to acquire images in a compressed form such that more
information can be transferred beyond the existing electronic hardware
bottleneck. To this end, we present a learnable optical
compression-decompression framework that learns content-specific features. The
proposed differentiable quantitative phase microscopy () first
uses learnable optical feature extractors as image compressors. The intensity
representation produced by these networks is then captured by the imaging
sensor. Finally, a reconstruction network running on electronic hardware
decompresses the QPM images. In numerical experiments, the proposed system
achieves compression of 64 while maintaining the SSIM of
and PSNR of dB on cells. The results demonstrated by our experiments
open up a new pathway for achieving end-to-end optimized (i.e., optics and
electronic) compact QPM systems that may provide unprecedented throughput
improvements
Data_Sheet_1_Highly sensitive quantitative phase microscopy and deep learning aided with whole genome sequencing for rapid detection of infection and antimicrobial resistance.docx
Current state-of-the-art infection and antimicrobial resistance (AMR) diagnostics are based on culture-based methods with a detection time of 48–96 h. Therefore, it is essential to develop novel methods that can do real-time diagnoses. Here, we demonstrate that the complimentary use of label-free optical assay with whole-genome sequencing (WGS) can enable rapid diagnosis of infection and AMR. Our assay is based on microscopy methods exploiting label-free, highly sensitive quantitative phase microscopy (QPM) followed by deep convolutional neural networks-based classification. The workflow was benchmarked on 21 clinical isolates from four WHO priority pathogens that were antibiotic susceptibility tested, and their AMR profile was determined by WGS. The proposed optical assay was in good agreement with the WGS characterization. Accurate classification based on the gram staining (100% recall for gram-negative and 83.4% for gram-positive), species (98.6%), and resistant/susceptible type (96.4%), as well as at the individual strain level (100% sensitivity in predicting 19 out of the 21 strains, with an overall accuracy of 95.45%). The results from this initial proof-of-concept study demonstrate the potential of the QPM assay as a rapid and first-stage tool for species, strain-level classification, and the presence or absence of AMR, which WGS can follow up for confirmation. Overall, a combined workflow with QPM and WGS complemented with deep learning data analyses could, in the future, be transformative for detecting and identifying pathogens and characterization of the AMR profile and antibiotic susceptibility.</p
Differentiable Microscopy Designs an All Optical Quantitative Phase Microscope
Ever since the first microscope by Zacharias Janssen in the late 16th
century, scientists have been inventing new types of microscopes for various
tasks. Inventing a novel architecture demands years, if not decades, worth of
scientific experience and creativity. In this work, we introduce Differentiable
Microscopy (), a deep learning-based design paradigm, to aid
scientists design new interpretable microscope architectures. Differentiable
microscopy first models a common physics-based optical system however with
trainable optical elements at key locations on the optical path. Using
pre-acquired data, we then train the model end-to-end for a task of interest.
The learnt design proposal can then be simplified by interpreting the learnt
optical elements. As a first demonstration, based on the optical 4- system,
we present an all-optical quantitative phase microscope (QPM) design that
requires no computational post-reconstruction. A follow-up literature survey
suggested that the learnt architecture is similar to the generalized phase
contrast method developed two decades ago. Our extensive experiments on
multiple datasets that include biological samples show that our learnt
all-optical QPM designs consistently outperform existing methods. We
experimentally verify the functionality of the optical 4- system based QPM
design using a spatial light modulator. Furthermore, we also demonstrate that
similar results can be achieved by an uninterpretable learning based method,
namely diffractive deep neural networks (D2NN). The proposed differentiable
microscopy framework supplements the creative process of designing new optical
systems and would perhaps lead to unconventional but better optical designs