Browsing by Author "Ngo, Khang"
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Item Biomechanical Markers of Lower Extremity Fatigue: A Literature Review(2024-03-21) Malhotra, Garima; Sotelo, Joseph; Ngo, Khang; Do, Khanh; Toledo, DavidPurpose: Lower-extremity (LE) muscular fatigue plays a pivotal role in injury occurrence across various domains, including sports, occupational settings, and daily activities. This is partly because fatigued muscles can absorb less energy before reaching the degree of stretch that causes injury. This review explores the significance of employing measurable biomechanical markers to assess lower extremity fatigue. Some studies have shown significant changes in post-fatigue functional tasks (vertical jump height), acceleration/velocity, postural stability (center of pressure), spatio-temporal gait metrics, and pressure distribution, but few reviews have compared these factors to identify the best predictors of fatigue. With the prevalence of musculoskeletal injuries, understanding the biomechanical changes associated with fatigue becomes imperative for predicting and preventing injury. Methods: PubMed, Google Scholar, and Scopus were searched using 20 distinct queries, which yielded 3,068 articles. The inclusion criteria used were: An experimental pre/post-test design, fatiguing protocol targeting lower extremities, recent studies after 2010, greater than 10 participants, data recorded via force plates or IMU, and a healthy adult population. In total, 44 articles met the inclusion criteria. Results: Of the included studies, CoP changes were dependent on the protocol used. High-intensity fatigue protocols were associated with significant increases in postural instability, measured by CoP displacement, velocity, and variability in both sagittal and coronal planes, but CoP measures were inconclusive in low-moderate intensity fatiguing protocols. When observing Spatio-Temporal gait metrics, the literature showed a strong positive correlation between increased ground contact time and fatigue. Stride length was negatively correlated with fatigue. Step width variability and single stance time were increased following fatigue. In terms of pressure distributions, increasing muscle fatigue was correlated with increases in plantar pressure. Peak pressure and force of metatarsals I - V were inconclusive, attributable to varying rear versus forefoot strike patterns, however, medial and lateral heel peak pressures consistently increased with muscle fatigue. In line with these findings, various studies demonstrated that increasing fatigue correlated with kinetic parameters such as increased stride cadence and, consequently, increased tibial acceleration. Prolonged or repetitive exposure to elevated tibial acceleration levels may be associated with greater impact forces and loading on the lower extremities, potentially contributing to muscle fatigue and elevating the risk of overuse injuries. Functional tests, such as jump height, showed a significant negative relationship with fatigue regardless of protocol intensity. A study comparing different jump height modalities and fatigue showed the highest repeatability and immediate/prolonged fatigue produced changes with CMJ, supported by its widespread use as an indicator for fatigue. Conclusions: We consistently found significant increases in sagittal plane CoP velocity, ground contact time, heel loading, and tibial acceleration with a significant decrease in CMJ height following LE fatigue. Limitations included variability in the fatigue protocols used and limited research that met inclusion criteria. In the future, these results can have implications for the development of wearables to track fatigue in athletes to decrease the incidence of injury.Item The Reliability of Artificial Intelligence in Prioritizing Management of Diabetic Macular Edema: A Comparative Study with 2 Retina Specialists(2024-03-21) Morcilla, Jericho; Cao, Jessica; Fan, Kenneth; Rahman, Effie; Ngo, Khang; Patel, Sagar; Chaudhary, Varun; Wykoff, CharlesPurpose There are currently no studies evaluating an Artificial Intelligence (AI) Large Language Model’s (LLM) reliability for ordinally prioritizing patients. In this study, we assess the ChatGPT Plus model (GPT-4) for prioritizing Diabetic Macular Edema (DME) patients, comparing its performance to that of two board-certified retina specialists (RS). We also investigate 2 additional questions: key variables for evaluators (GPT-4 and 2 RS) in DME prioritization, and the impact of incomplete clinical profiles on inter-evaluator agreement. Methods We used anonymized DME data from Retina Consultants of Texas to create 28 patient profiles. These profiles were divided into 4 sets based on ascending Diabetic Retinopathy (DR) severity (Set 1), Central Subfield Thickness (CST) (Set 2), modified Best Corrected Visual Acuity (BCVA) (Set 3), and a randomly organized control set (Set 4). We intentionally modified BCVA in Set 3, resulting in clinically incomplete patient profiles. 2 RS and GPT-4 prioritized patients in each set (e.g., Set 1) according to least to most treatment needs. We calculated the mean Cohen's Kappa (k) across all 4 sets to measure agreement between the 2 RS and the 2 RS with GPT-4 (k = 0.40–0.59 (weak), 0.60–0.79 (moderate), 0.80–0.90 (strong), >0.90 (almost-perfect)). Median RS evaluations were used to calculate individual set k as well as mean set k with GPT-4. Results Evaluations by the 2 RS (denoted RS1 and RS2) and GPT-4 show moderate agreement (Set 3 excluded: RS1-GPT-4 mean set k = 0.631; RS2-GPT-4 mean set k = 0.68). When using the median of RS evaluations, GPT-4's agreement with RS increased within the moderate range (Set 3 excluded: Median RS-GPT-4 mean set k = 0.77). Agreement between the 2 RS was weak (Set 3 excluded: RS1-RS2 mean set k = 0.48). The inclusion of Set 3 in mean set k calculations showed no clear impact. GPT-4 responses/explanations did not acknowledge clinical ambiguities in Set 3, noted by both RS in an optional comment box. Individual k values for Sets 1, 2, 3, and 4 were as follows: 0.58, 0.79, 0.72, 0.93. Conclusions The results reveal that GPT-4 shows promise in reliably prioritizing DME patients compared to RS. Ultimately, GPT-4 achieved moderate-strong (k ≥ 0.60) agreement with RS in DME prioritization. Interestingly, GPT-4’s increased agreement with median RS evaluations could indicate an ability to predict the RS consensus despite moderate disagreement between the 2 RS themselves. This is interesting considering an LLM’s inherent function — mathematically predict the average human response to a given text. This further supports GPT-4’s potential as a clinical tool, offering a grounded perspective in decision-making to assist more nuanced human judgment. Incomplete clinical profiles did not clearly impact agreement, possibly suggesting GPT-4’s adaptability. However, GPT-4 failed to recognize ambiguities in patient profiles that both RS noted. Therefore, human specialists may be better equipped to prevent erroneous decisions when evaluating incomplete or exceptional patient cases. Regarding key variables for prioritization, almost-perfect agreement in the control set (Set 4) warrants investigation into additional variables beyond what is explored in this study.Item Validation of Smart Insoles’ Fatigue Score, a Case Report(2024-03-21) Ngo, Khang; Toledo, David; Do, Khanh; Morcilla, Jericho; Kennedy, Shawn; Patterson, RitaBackground: Smart insoles give users valuable insights into their gait patterns, aiming to offer a cost-effective, reliable, accurate, and portable alternative to lab-based gait measurement using motion capture and force plates. Recent advancements have allowed IMU and pressure sensors (FSR) to be integrated directly into insoles, facilitating gait measurements outside of a lab environment. One promising application of smart insoles is to detect biomechanical changes in gait patterns and predict lower-extremity (LE) muscular fatigue. Reliable and accurate measurements of fatigue and recovery could play an essential role in injury prevention strategies and optimizing training intensity in various sports. Prior studies have used validated questionnaires such as the Rating of Fatigue (RoF) and Borg’s Rating of Perceived Exertion (RPE), which showed strong correlations with perceived fatigue and exertion ratings. Heart rate (HR) has also been frequently utilized among the objective measurements of fatigue. This case report aims to validate the fatigue score predicted by the smart insoles (LFS) against subjective and objective measures of fatigue such as RoF, RPE, and HR. Case Information: A 24-year-old male without existing gait abnormalities participated in a pre-test/post-test design with a progression-to-fatigue protocol consisting of multiple sets of repeated exercises until fatigue. The baseline (pre-evaluation) assessment included a 20m fast-paced walk and 2 static movement jumps (SMJ), followed by 2 countermovement jumps (CMJ). RoF, RPE, and HR measurements were collected at baseline and after each fatiguing set. Each fatigue set consisted of 6x 10m shuttle sprints followed by 5 vertical jumps consecutively without rest. Following each fatigue set, participants underwent post-evaluations that included the same exercises as the pre-evaluation. This approach enabled the insoles to analyze changes in gait from baseline due to the accumulation of fatigue. A 30-minute recovery session was completed after the participant reached a RoF = 10. The recovery session consisted of the RoF, RPE, HR, and post-evaluation measurements every 2 minutes for the first 10 minutes, then every 5 minutes for the remaining 20 minutes. Conclusion: The participant reached RoF = 10 after 6 repeated fatigue sets. Ground contact time (GCT), measured by the insoles, is one parameter that has been identified to inform the level of fatigue. In the fatiguing session, GCT was very strongly correlated with RoF and RPE (r = 0.971, p=.001); (r=0.986, p<0.001), respectively, but was not strongly correlated with HR (r= 0.543, p = 0.27). In the recovery session, GCT was strongly correlated with both RoF and HR (r =0.689, p=0.04); (r=0.723, p =0.028), respectively. RPE was a poor indicator of fatigue in the recovery session compared to RoF, GCT, and HR.GCT, along with other identified parameters, has the potential to be utilized in the development of the LFS. The LFS score analyzed changes in gait following fatigue and subsequently output a fatigue score for each set. Spearman’s rho and ICC were used to assess correlations and agreement between LFS, RoF, RPE, and HR. Data analysis was done using SPSS v29.0.