Realtime assessment of vascular occlusion and reperfusion in animal models of intraoperative imaging - a pilot study.

Objectives: Intraoperative monitoring of blood flow (BF) remains vital to guiding surgical decisions. Here, we report the use of SurgeON™ Blood Flow Monitor (BFM), a prototype system that attaches to surgical microscopes and implements laser speckle contrast imaging (LSCI) to noninvasively obtain and present vascular BF information in real-time within the microscope's eyepiece. Methods: The ability of SurgeON BFM to monitor BF status during reversible vascular occlusion procedures was investigated in two large animal models: occlusion of saphenous veins in six NZW rabbit hindlimbs and clipping of middle cerebral artery (MCA) branches in four Dorset sheep brain hemispheres. SurgeON BFM acquired, presented, and stored LSCI-based blood flow velocity index (BFVi) data and performed indocyanine green video angiography (ICG-VA) for corroboration. Results: Stored BFVi data were analyzed for each phase: pre-occlusion (baseline), with the vessel occluded (occlusion), and after reversal of occlusion (re-perfusion). In saphenous veins, BFVi relative to baseline reduced to 5.2±3.7 % during occlusion and returned to 102.9±14.9 % during re-perfusion. Unlike ICG-VA, SurgeON BFM was able to monitor reduced BFVi and characterize re-perfusion robustly during five serial occlusion procedures conducted 2-5 min apart on the same vessel. Across four sheep MCA vessels, BFVi reduced to 18.6±7.7 % and returned to 120.1±27.8 % of baseline during occlusion and re-perfusion phases, respectively. Conclusions: SurgeON BFM can noninvasively monitor vascular occlusion status and provide intuitive visualization of BF information in real-time to an operating surgeon. This technology may find application in vascular, plastic, and neurovascular surgery.

A Label-Efficient Framework for Automated Sinonasal CT Segmentation in Image-Guided Surgery.

OBJECTIVE: Segmentation, the partitioning of patient imaging into multiple, labeled segments, has several potential clinical benefits but when performed manually is tedious and resource intensive. Automated deep learning (DL)-based segmentation methods can streamline the process. The objective of this study was to evaluate a label-efficient DL pipeline that requires only a small number of annotated scans for semantic segmentation of sinonasal structures in CT scans. STUDY DESIGN: Retrospective cohort study. SETTING: Academic institution. METHODS: Forty CT scans were used in this study including 16 scans in which the nasal septum (NS), inferior turbinate (IT), maxillary sinus (MS), and optic nerve (ON) were manually annotated using an open-source software. A label-efficient DL framework was used to train jointly on a few manually labeled scans and the remaining unlabeled scans. Quantitative analysis was then performed to obtain the number of annotated scans needed to achieve submillimeter average surface distances (ASDs). RESULTS: Our findings reveal that merely four labeled scans are necessary to achieve median submillimeter ASDs for large sinonasal structures-NS (0.96 mm), IT (0.74 mm), and MS (0.43 mm), whereas eight scans are required for smaller structures-ON (0.80 mm). CONCLUSION: We have evaluated a label-efficient pipeline for segmentation of sinonasal structures. Empirical results demonstrate that automated DL methods can achieve submillimeter accuracy using a small number of labeled CT scans. Our pipeline has the potential to improve pre-operative planning workflows, robotic- and image-guidance navigation systems, computer-assisted diagnosis, and the construction of statistical shape models to quantify population variations. LEVEL OF EVIDENCE: N/A.

SVPath: A Deep Learning Tool for Analysis of Stria Vascularis from Histology Slides.

INTRODUCTION: The stria vascularis (SV) may have a significant role in various otologic pathologies. Currently, researchers manually segment and analyze the stria vascularis to measure structural atrophy. Our group developed a tool, SVPath, that uses deep learning to extract and analyze the stria vascularis and its associated capillary bed from whole temporal bone histopathology slides (TBS). METHODS: This study used an internal dataset of 203 digitized hematoxylin and eosin-stained sections from a normal macaque ear and a separate external validation set of 10 sections from another normal macaque ear. SVPath employed deep learning methods YOLOv8 and nnUnet to detect and segment the SV features from TBS, respectively. The results from this process were analyzed with the SV Analysis Tool (SVAT) to measure SV capillaries and features related to SV morphology, including width, area, and cell count. Once the model was developed, both YOLOv8 and nnUnet were validated on external and internal datasets. RESULTS: YOLOv8 implementation achieved over 90% accuracy for cochlea and SV detection. nnUnet SV segmentation achieved a DICE score of 0.84-0.95; the capillary bed DICE score was 0.75-0.88. SVAT was applied to compare both the ears used in the study. There was no statistical difference in SV width, SV area, and average area of capillary between the two ears. There was a statistical difference between the two ears for the cell count per SV. CONCLUSION: The proposed method accurately and efficiently analyzes the SV from temporal histopathology bone slides, creating a platform for researchers to understand the function of the SV further.

A Deep Learning Framework for Analysis of the Eustachian Tube and the Internal Carotid Artery.

OBJECTIVE: Obtaining automated, objective 3-dimensional (3D) models of the Eustachian tube (ET) and the internal carotid artery (ICA) from computed tomography (CT) scans could provide useful navigational and diagnostic information for ET pathologies and interventions. We aim to develop a deep learning (DL) pipeline to automatically segment the ET and ICA and use these segmentations to compute distances between these structures. STUDY DESIGN: Retrospective cohort. SETTING: Tertiary referral center. METHODS: From a database of 30 CT scans, 60 ET and ICA pairs were manually segmented and used to train an nnU-Net model, a DL segmentation framework. These segmentations were also used to develop a quantitative tool to capture the magnitude and location of the minimum distance point (MDP) between ET and ICA. Performance metrics for the nnU-Net automated segmentations were calculated via the average Hausdorff distance (AHD) and dice similarity coefficient (DSC). RESULTS: The AHD for the ET and ICA were 0.922 and 0.246 mm, respectively. Similarly, the DSC values for the ET and ICA were 0.578 and 0.884. The mean MDP from ET to ICA in the cartilaginous region was 2.6 mm (0.7-5.3 mm) and was located on average 1.9 mm caudal from the bony cartilaginous junction. CONCLUSION: This study describes the first end-to-end DL pipeline for automated ET and ICA segmentation and analyzes distances between these structures. In addition to helping to ensure the safe selection of patients for ET dilation, this method can facilitate large-scale studies exploring the relationship between ET pathologies and the 3D shape of the ET.

A Novel Instrument Holder to Improve Operative Efficiency in Endoscopic Laryngeal and Airway Surgery.

OBJECTIVE: Receiving instruments from surgical technicians during endoscopic laryngeal and airway microsurgery (ELAM) has challenges including repeated, expeditious handling of delicate instruments and passing them to the surgeon's hand opposite of where the surgical assistant is standing. Optimizing this interaction may reduce surgical errors and improve operative efficiency. METHODS: A proprietary ELAM instrument holder was attached to both sides of the operating room bed. The device consisted of an articulating arm with custom silicone inserts mounted on a tray (storing up to three endoscopic instruments). ELAM cases were randomized to be performed either with (device) or without the holder (control). Using custom software, instrument pass time (IPT), instrument drop rate (IDR), and communication errors (eg handing incorrect instruments) were manually recorded. Qualitative use metrics relating to overall device satisfaction were also obtained. RESULTS: Data were collected from 25 device and 23 control cases among three different laryngologists. Average IPT was nearly three times quicker for the device (0.80 s, n = 1175 passes) compared with controls (2.09 s, n = 1208 passes) [p < 0.001]. IPT interquartile range was five times higher for control (1.65 s) versus device cases (0.42 s). IDR was not significantly different [p = 0.48]; however, device cases had significantly lower communication errors compared to control cases [p = 0.01]. Surgeons and surgical assistants were similarly satisfied with the device on a 5-point Likert scale (mean: 4.2/5, standard deviation: 0.92). CONCLUSION: The proposed endoscopic instrument holder can improve ELAM operative workflow by reducing instrument passing time and variability without increasing IDR. LEVEL OF EVIDENCE: 2 Laryngoscope, 133:3492-3498, 2023.