Intraoperative characterization of cardiac tissue: the potential of light scattering spectroscopy.

Significance: Damage to the cardiac conduction system remains one of the most significant risks associated with surgical interventions to correct congenital heart disease. This work demonstrates how light-scattering spectroscopy (LSS) can be used to non-destructively characterize cardiac tissue regions. Aim: To present an approach for associating tissue composition information with location-specific LSS data and further evaluate an LSS and machine learning system as a method for non-destructive tissue characterization. Approach: A custom LSS probe was used to gather spectral data from locations across 14 excised human pediatric nodal tissue samples (8 sinus nodes, 6 atrioventricular nodes). The LSS spectra were used to train linear and neural-network-based regressor models to predict tissue composition characteristics derived from the 3D models. Results: Nodal tissue region nuclear densities were reported. A linear model trained to regress nuclear density from spectra achieved a prediction r-squared of 0.64 and a concordance correlation coefficient of 0.78. Conclusions: These methods build on previous studies suggesting that LSS measurements combined with machine learning signal processing can provide clinically relevant cardiac tissue composition.

3D models of the cardiac conduction system in healthy neonatal human hearts.

Iatrogenic damage to the cardiac conduction system (CCS) remains a significant risk during congenital heart surgery. Current surgical best practice involves using superficial anatomical landmarks to locate and avoid damaging the CCS. Prior work indicates inherent variability in the anatomy of the CCS and supporting tissues. This study introduces high-resolution, 3D models of the CCS in normal pediatric human hearts to evaluate variability in the nodes and surrounding structures. Human pediatric hearts were obtained with an average donor age of 2.7 days. A pipeline was developed to excise, section, stain, and image atrioventricular (AVN) and sinus nodal (SN) tissue regions. A convolutional neural network was trained to enable precise multi-class segmentation of whole-slide images, which were subsequently used to generate high- resolution 3D tissue models. Nodal tissue region models were created. All models (10 AVN, 8 SN) contain tissue composition of neural tissue, vasculature, and nodal tissues at micrometer resolution. We describe novel nodal anatomical variations. We found that the depth of the His bundle in females was on average 304 µm shallower than those of male patients. These models provide surgeons with insight into the heterogeneity of the nodal regions and the intricate relationships between the CCS and surrounding structures.

Systematic Mapping of Conduction Tissue Regions During Congenital Heart Surgery.

PURPOSE: Damage to the cardiac conduction system is a major risk of congenital cardiac surgery. Localization of the conduction system is commonly based on anatomic landmarks, which are variable in congenital heart diseases. We introduce a novel technique for identification of conduction tissue regions based on real-time fiberoptic confocal microscopy. DESCRIPTION: We developed a fiberoptic confocal microscopy-based technique to document conduction tissue regions and deployed it in pediatric patients undergoing repair of common congenital heart defects. The technique applies clockface schematics for intraoperative documentation of the location of conduction tissue regions. EVALUATION: We created clockface schematics for 11 patients with ventricular septal defects, 6 with tetralogy of Fallot, and 10 with atrioventricular canal defects. The approach revealed substantial variability in the location of the conduction system in hearts with congenital defects. The clockface schematics were used to create plans for subsequent surgical repair. CONCLUSIONS: The clockface schematic provides a reliable fiducial system to document and communicate variability of conduction tissue regions in the heart and applies this information for decision-making during congenital cardiac surgery.

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.

Toward detection of conduction tissue during cardiac surgery: Light at the end of the tunnel?

Postoperative conduction block requiring lifetime pacemaker placement continues to be a considerable source of morbidity for patients undergoing repair of congenital heart defects. Damage to the cardiac conduction system (CCS) during surgical procedures is thought to be a major cause of conduction block. Intraoperative identification and avoidance of the CCS is thus a key strategy to improve surgical outcomes. A number of approaches have been developed to avoid conduction tissue damage and mitigate morbidity. Here we review the historical and contemporary approaches for identification of conduction tissue during cardiac surgery. The established approach for intraoperative identification is based on anatomic landmarks established in extensive histologic studies of normal and diseased heart. We focus on landmarks to identify the sinus and atrioventricular nodes during cardiac surgery. We also review technologies explored for intraoperative tissue identification, including electrical impedance measurements and electrocardiography. We describe new optical approaches, in particular, and optical spectroscopy and fiberoptic confocal microscopy (FCM) for identification of CCS regions and working myocardium during surgery. As a template for translation of future technology developments, we describe research and regulatory pathways to translate FCM for cardiac surgery. We suggest that along with more robust approaches to surgeon training, including awareness of fundamental anatomic studies, optical approaches such as FCM show promise in aiding surgeons with repairs of heart defects. In particular, for complex defects, these approaches can complement landmark-based identification of conduction tissue and thus help to avoid injury to the CCS due to surgical procedures.

Localization of the sinoatrial and atrioventricular nodal region in neonatal and juvenile ovine hearts.

Localization of the components of the cardiac conduction system (CCS) is essential for many therapeutic procedures in cardiac surgery and interventional cardiology. While histological studies provided fundamental insights into CCS localization, this information is incomplete and difficult to translate to aid in intraprocedural localization. To advance our understanding of CCS localization, we set out to establish a framework for quantifying nodal region morphology. Using this framework, we quantitatively analyzed the sinoatrial node (SAN) and atrioventricular node (AVN) in ovine with postmenstrual age ranging from 4.4 to 58.3 months. In particular, we studied the SAN and AVN in relation to the epicardial and endocardial surfaces, respectively. Using anatomical landmarks, we excised the nodes and adjacent tissues, sectioned those at a thickness of 4 µm at 100 µm intervals, and applied Masson's trichrome stain to the sections. These sections were then imaged, segmented to identify nodal tissue, and analyzed to quantify nodal depth and superficial tissue composition. The minimal SAN depth ranged between 20 and 926 µm. AVN minimal depth ranged between 59 and 1192 µm in the AVN extension region, 49 and 980 µm for the compact node, and 148 and 888 µm for the transition to His Bundle region. Using a logarithmic regression model, we found that minimal depth increased logarithmically with age for the AVN (R2 = 0.818, P = 0.002). Also, the myocardial overlay of the AVN was heterogeneous within different regions and decreased with increasing age. Age associated alterations of SAN minimal depth were insignificant. Our study presents examples of characteristic tissue patterns superficial to the AVN and within the SAN. We suggest that the presented framework provides quantitative information for CCS localization. Our studies indicate that procedural methods and localization approaches in regions near the AVN should account for the age of patients in cardiac surgery and interventional cardiology.

Subsequent Use of a Pressure Sensor to Record Intra-Abdominal Pressure After Maximum Vaginal Closure Force in a Clinical Trial.

Pelvic floor disorders are caused by weakening or damage to the tissues lining the bottom of the abdominal cavity. These disorders affect nearly 1 in every 4 women in the United States and symptoms that drastically diminish a patient's quality of life. Vaginal closure force is a good measure of pelvic health, but current vaginal dynamometers were not designed for the rigors of hospital reprocessing, often failing due to sensor degradation through repeated sterilization processes. In order to obtain measurements of vaginal closure force in a large study, we designed a vaginal dynamometer that utilizes a removable intra-abdominal sensor already in production for the study. The sensor's existing data acquisition system was modified to transmit to a tablet allowing the user to view data in real-time. The new speculum design allowed a single sensor to measure vaginal closure force before being used to collect intra-abdominal pressure data in the same study visit. The measurements taken with the new speculum were similar to measurements taken with a previously reported vaginal dynamometer.

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.