Erratum to "Optical Detection of Distal Lung Enzyme Activity in Human Inflammatory Lung Disease".

[This corrects the article DOI: 10.34133/2021/9834163.].

Toward cardiac tissue characterization using machine learning and light-scattering spectroscopy.

SIGNIFICANCE: The non-destructive characterization of cardiac tissue composition provides essential information for both planning and evaluating the effectiveness of surgical interventions such as ablative procedures. Although several methods of tissue characterization, such as optical coherence tomography and fiber-optic confocal microscopy, show promise, many barriers exist that reduce effectiveness or prevent adoption, such as time delays in analysis, prohibitive costs, and limited scope of application. Developing a rapid, low-cost non-destructive means of characterizing cardiac tissue could improve planning, implementation, and evaluation of cardiac surgical procedures. AIM: To determine whether a new light-scattering spectroscopy (LSS) system that analyzes spectra via neural networks is capable of predicting the nuclear densities (NDs) of ventricular tissues. APPROACH: We developed an LSS system with a fiber-optics probe and applied it for measurements on cardiac tissues from an ovine model. We quantified the ND in the cardiac tissues using fluorescent labeling, confocal microscopy, and image processing. Spectra acquired from the same cardiac tissues were analyzed with spectral clustering and convolutional neural networks (CNNs) to assess the feasibility of characterizing the ND of tissue via LSS. RESULTS: Spectral clustering revealed distinct groups of spectra correlated to ranges of ND. CNNs classified three groups of spectra with low, medium, or high ND with an accuracy of 95.00 ± 11.77 % (mean and standard deviation). Our analyses revealed the sensitivity of the classification accuracy to wavelength range and subsampling of spectra. CONCLUSIONS: LSS and machine learning are capable of assessing ND in cardiac tissues. We suggest that the approach is useful for the diagnosis of cardiac diseases associated with changes of ND, such as hypertrophy and fibrosis.

Towards Intraoperative Quantification of Atrial Fibrosis Using Light-Scattering Spectroscopy and Convolutional Neural Networks.

Light-scattering spectroscopy (LSS) is an established optical approach for characterization of biological tissues. Here, we investigated the capabilities of LSS and convolutional neural networks (CNNs) to quantitatively characterize the composition and arrangement of cardiac tissues. We assembled tissue constructs from fixed myocardium and the aortic wall with a thickness similar to that of the atrial free wall. The aortic sections represented fibrotic tissue. Depth, volume fraction, and arrangement of these fibrotic insets were varied. We gathered spectra with wavelengths from 500-1100 nm from the constructs at multiple locations relative to a light source. We used single and combinations of two spectra for training of CNNs. With independently measured spectra, we assessed the accuracy of the CNNs for the classification of tissue constructs from single spectra and combined spectra. Combined spectra, including the spectra from fibers distal from the illumination fiber, typically yielded the highest accuracy. The maximal classification accuracy of the depth detection, volume fraction, and permutated arrangements was (mean ± standard deviation (stddev)) 88.97 ± 2.49%, 76.33 ± 1.51%, and 84.25 ± 1.88%, respectively. Our studies demonstrate the reliability of quantitative characterization of tissue composition and arrangements using a combination of LSS and CNNs. The potential clinical applications of the developed approach include intraoperative quantification and mapping of atrial fibrosis, as well as the assessment of ablation lesions.

Optical Detection of Distal Lung Enzyme Activity in Human Inflammatory Lung Disease.

Objective and Impact Statement. There is a need to develop platforms delineating inflammatory biology of the distal human lung. We describe a platform technology approach to detect in situ enzyme activity and observe drug inhibition in the distal human lung using a combination of matrix metalloproteinase (MMP) optical reporters, fibered confocal fluorescence microscopy (FCFM), and a bespoke delivery device. Introduction. The development of new therapeutic agents is hindered by the lack of in vivo in situ experimental methodologies that can rapidly evaluate the biological activity or drug-target engagement in patients. Methods. We optimised a novel highly quenched optical molecular reporter of enzyme activity (FIB One) and developed a translational pathway for in-human assessment. Results. We demonstrate the specificity for matrix metalloproteases (MMPs) 2, 9, and 13 and probe dequenching within physiological levels of MMPs and feasibility of imaging within whole lung models in preclinical settings. Subsequently, in a first-in-human exploratory experimental medicine study of patients with fibroproliferative lung disease, we demonstrate, through FCFM, the MMP activity in the alveolar space measured through FIB One fluorescence increase (with pharmacological inhibition). Conclusion. This translational in situ approach enables a new methodology to demonstrate active drug target effects of the distal lung and consequently may inform therapeutic drug development pathways.

Towards Automated Quantification of Atrial Fibrosis in Images from Catheterized Fiber-Optics Confocal Microscopy Using Convolutional Neural Networks.

Clinical approaches for quantification of atrial fibrosis are currently based on digital image processing of magnetic resonance images. Here, we introduce and evaluate a comprehensive framework based on convolutional neural networks for quantifying atrial fibrosis from images acquired with catheterized fiber-optics confocal microscopy (FCM). FCM images in three regions of the atria were acquired in the beating heart in situ in an established transgenic animal model of atrial fibrosis. Fibrosis in the imaged regions was histologically assessed in excised tissue. FCM images and their corresponding histologically-assessed fibrosis levels were used for training of a convolutional neural network. We evaluated the utility and performance of the convolutional neural networks by varying parameters including image dimension and training batch size. In general, we observed that the root-mean square error (RMSE) of the predicted fibrosis was decreased with increasing image dimension. We achieved a RMSE of 2.6% and a Pearson correlation coefficient of 0.953 when applying a network trained on images with a dimension of 400 × 400 pixels and a batch size of 128 to our test image set. The findings indicate feasibility of our approach for fibrosis quantification from images acquired with catheterized FCM using convolutional neural networks. We suggest that the developed framework will facilitate translation of catheterized FCM into a clinical approach that complements current approaches for quantification of atrial fibrosis.

Development of an alveolar transbronchial catheter for concurrent fiber optics based imaging and fluid delivery.

Optical molecular imaging is an emerging field and high resolution optical imaging of the distal lung parenchyma has been made possible with the advent of clinically approved fiber based imaging modalities. However, currently, there is no single method of allowing the simultaneous imaging and delivery of targeted molecular imaging agents. The objective of this research is to create a catheterized device capable of fulfilling this need. We describe the rationale, development, and validation in ex vivo ovine lung to near clinical readiness of a triple lumen bronchoscopy catheter that allows concurrent imaging and fluid delivery, with the aim of clinical use to deliver multiple fluorescent compounds to image alveolar pathology. Using this device, we were able to produce high-quality images of bacterial infiltrates in ex-vivo ovine lung within 60 seconds of instilling a single microdose of (<100 mcgs) imaging agent. This has many advantages for future clinical usage over the current state of the art.