DeepLabCut increases markerless tracking efficiency in X-ray video analysis of rodent locomotion.

Despite the prevalence of rat models to study human disease and injury, existing methods for quantifying behavior through skeletal movements are problematic owing to skin movement inaccuracies associated with optical video analysis, or require invasive implanted markers or time-consuming manual rotoscoping for X-ray video approaches. We examined the use of a machine learning tool, DeepLabCut, to perform automated, markerless tracking in bi-planar X-ray videos of locomoting rats. Models were trained on 590 pairs of video frames to identify 19 unique skeletal landmarks of the pelvic limb. Accuracy, precision and time savings were assessed. Machine-identified landmarks deviated from manually labeled counterparts by 2.4±0.2 mm (n=1710 landmarks). DeepLabCut decreased analysis time by over three orders of magnitude (1627×) compared with manual labeling. Distribution of these models may enable the processing of a large volume of accurate X-ray kinematics locomotion data in a fraction of the time without requiring surgically implanted markers.

Acoustic Emissions as a Non-invasive Biomarker of the Structural Health of the Knee.

The longitudinal assessment of joint health is a long-standing issue in the management of musculoskeletal injuries. The acoustic emissions (AEs) produced by joint articulation could serve as a biomarker for joint health assessment, but their use has been limited by a lack of mechanistic understanding of their creation. In this paper, we investigate that mechanism using an injury model in human lower-limb cadavers, and relate AEs to joint kinematics. Using our custom joint sound recording system, we recorded the AEs from nine cadaver legs in four stages: at baseline, after a sham surgery, after a meniscus tear, and post-meniscectomy. We compare the resulting AEs using their b-values. We then compare joint anatomy/kinematics to the AEs using the X-ray reconstruction of moving morphology (XROMM) technique. After the meniscus tear the number and amplitude of the AE peaks greatly increased from baseline and sham (b-value = 1.33 ± 0.15; p < 0.05). The XROMM analysis showed a close correlation between the minimal inter-joint distances (0.251 ± 0.082 cm during extension, 0.265 ± .003 during flexion, at 145°) and a large increase in the AEs. This work provides key insight into the nature of joint AEs, and details a novel technique and analysis for recording and interpreting these biosignals.

Evidence for startle as a measurable behavioral indicator of motor learning.

The ability of the classic startle reflex to evoke voluntarily prepared movement involuntarily has captured the attention of neuroscientists for its wide-ranging functional utility and potential uses in patient populations. To date, there is only one documented task resistant to the startReact phenomenon-index finger abduction. Previous reports have suggested the lack of startReact is due to different neural mechanisms driving individuated finger movement and more proximal joint control (e.g. elbow, wrist movement). However, an alternative hypothesis exists. Though not particularly difficult to execute, isolated index finger abduction is rarely performed during activities of daily living and is not a natural correlate to common individuated finger tasks. We propose that startReact can be evoked during individuated finger movements but only during tasks that are highly trained or familiar. The objective of this study was to determine the impact of a 2-week training regimen on the ability to elicit startReact. We found evidence in support of our hypothesis that following training, individuated movements of the hands (specifically index finger abduction) become susceptible to startReact. This is significant not only because it indicates that individuated finger movements are in fact amenable to startReact, but also that startle has differential response characteristics in novel tasks compared to highly trained tasks suggesting that startle is a measurable behavioral indicator of motor learning.

Promoting a Restrictive Intraoperative Transfusion Strategy: The Influence of a Transfusion Guideline and a Novel Software Tool.

BACKGROUND: The effect of neither transfusion guidelines nor decision support tools on intraoperative transfusion has been previously evaluated. The University of Michigan introduced a transfusion guideline in 2009, and in 2011, the Department of Anesthesiology developed a transfusion decision support tool. The primary aim of this study was to assess the associations of the transfusion guideline and the optional use of the software transfusion tool with intraoperative behaviors; pretransfusion hematocrit assessment (whether or not a hematocrit was checked before each red cell unit) and restrictive red cell use (withholding transfusion unless the hematocrit was ≤21%). METHODS: This was a before-after retrospective study without a concurrent control group of patients transfused 1-3 units of red cells intraoperatively. Three phases were studied to provide data both before and after the implementation of the transfusion guideline and the intraoperative software tool. Within each phase, trends of checking hematocrits before transfusion and restrictive transfusion were charted against time. F tests were used to measure differences of slopes. The difference between means of each phase was measured using Mann-Whitney U tests. Independent associations were measured using mixed-effects multivariable logistic regression. A secondary outcome analysis was conducted for 30-day mortality, myocardial infarction, renal injury, and their combination. RESULTS: The transfusion guideline was associated with increased pretransfusion hematocrit evaluation (67.4%, standard deviation [SD] 3.9 vs 76.5%, SD 2.7; P < .001) and restrictive transfusion practice (14.0%, SD 7.4 vs 33.3%, SD 4.4; P = .001). After adjustment for confounders, the guideline phase was independently associated with increased hematocrit checking (odds ratio, 1.72; 95% confidence interval, 1.46-2.03; P < .001) and restrictive red cell transfusion (odds ratio, 2.95; 95% confidence interval, 2.46-3.54; P < .001). The software tool was not associated with either transfusion behavior. There was no significant change in the rate of renal injury (16.06%), myocardial injury (4.93%), 30-day mortality (5.47%), or a composite (21.90%). CONCLUSIONS: The introduction of a transfusion guideline was independently associated with increased intraoperative pretransfusion hematocrit assessment and restrictive transfusion. The use of a software tool did not further influence either behavior.

Airway exchange failure and complications with the use of the Cook Airway Exchange Catheter®: a single center cohort study of 1177 patients.

There are limited data on rates of failure and airway injury with the use of airway exchange catheters. We performed a single-center retrospective analysis of airway exchange catheters to determine the incidence and associated factors for tube exchange failure and airway injury. Among 1177 cases, failed intubation during attempted tube exchange was noted in 73/527 (13.8%). Airway exchange failure rates were greatest during exchange catheter use for double-lumen tube insertion and when intubation was attempted over the catheter postoperatively. Pneumothorax was noted after 1.5% of attempted tube exchanges. Difficult tube exchange was encountered in 6 of 8 patients with pneumothorax.