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Abstract: We previously conducted a single-arm, prospective study in which 31 patients (mean age [and standard deviation], 42.5 ± 11.3 years) with cartilage lesions were treated with use of the BioPoly Partial Resurfacing Knee Implant. Treatment outcomes were compared with those reported for the standard of care, microfracture. We found that the mean KOOS (Knee injury and Osteoarthritis Outcome Score) Quality of Life score at 5 years in the BioPoly cohort was noninferior to (p = 0.004), and indeed greater than (p = 0.021), that in the microfracture cohort. The BioPoly cohort demonstrated improvement in the mean scores for all KOOS domains at every postoperative time point (p < 0.025). The mean score for the visual analog scale (VAS) for pain significantly improved (p < 0.025) at all time points up to 4 years and trended toward significant improvement at 5 years (p = 0.027). This study indicated that the BioPoly implant was safe, provided significant improvement starting at 6 months and continuing to 5 years, and provided greater improvement than microfracture for some outcome measures., Level of Evidence: Therapeutic Level IV. See Instructions for Authors for a complete description of levels of evidence., Competing Interests: Disclosure: The Disclosure of Potential Conflicts of Interest forms are provided with the online version of the article (http://links.lww.com/JBJSOA/A564)., (Copyright © 2023 The Authors. Published by The Journal of Bone and Joint Surgery, Incorporated. All rights reserved.)

Background: Parkinson's disease (PD) is the second most common neurodegenerative disorder whose prevalence rises with age, yet clinical diagnosis is still a challenging task due to similar manifestations of other neurodegenerative movement disorders. In untreated patients or those with unclear responses to medication, correct percentages of early diagnoses go as low as 26%. Technology has been used in various forms to facilitate discerning between persons with PD and healthy individuals, but much less work has been dedicated to separating PD and atypical parkinsonisms., Methods: A wearable system was developed based on inertial sensors that capture the movements of fingers during repetitive finger tapping. A k-nearest-neighbor classifier was used on features extracted from gyroscope recordings for quick aid in differential diagnostics, discerning patients with PD, progressive supranuclear palsy (PSP), multiple system atrophy (MSA) and healthy controls (HC)., Results: The overall classification accuracy achieved was 85.18% in the multiclass setup. MSA and HC groups were the easiest to discern (100%), while PSP was the most elusive diagnosis, as some patients were incorrectly assigned to MSA and HC groups., Conclusions: The system shows potential for use as a tool for quick diagnostic aid, and in the era of big data, offers a means of standardization of data collection that could allow scientists to aggregate multi-center data for further research., (© 2023 The Authors.)

Objective: The goal of this study was to analyze how depression associated with Parkinson's disease (PD) affected gait variability in these patients using a dual-task paradigm. Additionally, the dependency of the executive functions and the impact of depression on gait variability were analyzed., Patients and Methods: Three subject groups were included: patients with PD, but no depression (PD-NonDep; 14 patients), patients with both PD and depression (PD-Dep; 16 patients) and healthy controls (HC; 15 subjects). Gait was recorded using the wireless sensors. The participants walked under four conditions: single-task, motor dual- task, cognitive dual-task, and combined dual-task. Variability of stride length, stride duration, and swing time was calculated and analyzed using the statistical methods., Results: Variability of stride duration and stride length were not significantly different between PD-Dep and PD-NonDep patients. The linear mixed model showed that swing time variability was statistically significantly higher in PD-Dep patients compared to controls (p = 0.001). Hamilton Disease Rating Scale scores were significantly correlated with the swing time variability (p = 0.01). Variability of all three parameters of gait was significantly higher while performing combined or cognitive task and this effect was more pronounced in PD-Dep group of patients., Conclusions: Depression in PD was associated with swing time variability, and this effect was more prominent while performing a dual-task., Significance: Diagnosing and treating depression might be important for gait improvement and fall reduction in PD patients., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Artificial intelligence, specifically machine learning, has found numerous applications in computer-aided diagnostics, monitoring and management of neurodegenerative movement disorders of parkinsonian type. These tasks are not trivial due to high inter-subject variability and similarity of clinical presentations of different neurodegenerative disorders in the early stages. This paper aims to give a comprehensive, high-level overview of applications of artificial intelligence through machine learning algorithms in kinematic analysis of movement disorders, specifically Parkinson's disease (PD). We surveyed papers published between January 2007 and January 2019, within online databases, including PubMed and Science Direct, with a focus on the most recently published studies. The search encompassed papers dealing with the implementation of machine learning algorithms for diagnosis and assessment of PD using data describing motion of upper and lower extremities. This systematic review presents an overview of 48 relevant studies published in the abovementioned period, which investigate the use of artificial intelligence for diagnostics, therapy assessment and progress prediction in PD based on body kinematics. Different machine learning algorithms showed promising results, particularly for early PD diagnostics. The investigated publications demonstrated the potentials of collecting data from affordable and globally available devices. However, to fully exploit artificial intelligence technologies in the future, more widespread collaboration is advised among medical institutions, clinicians and researchers, to facilitate aligning of data collection protocols, sharing and merging of data sets., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Wearable sensors and advanced algorithms can provide significant decision support for clinical practice. Currently, the motor symptoms of patients with neurological disorders are often visually observed and evaluated, which may result in rough and subjective quantification. Using small inertial wearable sensors, fine repetitive and clinically important movements can be captured and objectively evaluated. In this paper, a new methodology is designed for objective evaluation and automatic scoring of bradykinesia in repetitive finger-tapping movements for patients with idiopathic Parkinson's disease and atypical parkinsonism. The methodology comprises several simple and repeatable signal-processing techniques that are applied for the extraction of important movement features. The decision support system consists of simple rules designed to match universally defined criteria that are evaluated in clinical practice. The accuracy of the system is calculated based on the reference scores provided by two neurologists. The proposed expert system achieved an accuracy of 88.16% for files on which neurologists agreed with their scores. The introduced system is simple, repeatable, easy to implement, and can provide good assistance in clinical practice, providing a detailed analysis of finger-tapping performance and decision support for symptom evaluation.

Background: Current treatments for focal chondral and osteochondral lesions of the femoral condyle have been associated with variable outcomes. We conducted a clinical trial of the BioPoly RS Partial Resurfacing Knee Implant to address this unmet need., Methods: We performed a single-arm, prospective study in which 33 patients with focal cartilage lesions affecting the femoral condyle were managed with the BioPoly RS Partial Resurfacing Knee Implant. Knee injury and Osteoarthritis Outcome Score (KOOS) scores, a visual analog scale (VAS) for pain, the Short Form-36 (SF-36) physical component score , and the Tegner activity score were used to assess outcomes preoperatively and at 6 months, 1 year, and 2 years postoperatively. The KOOS outcomes at 2 years were compared with historical outcomes following microfracture treatment., Results: We found significant and clinically meaningful improvements in the KOOS scores, VAS pain score, and SF-36 physical component score (p < 0.025) when the values at all 3 postoperative time points were compared with the preoperative scores, and we also found significant improvements when the Tegner activity score at 2 years was compared with the preoperative score (p < 0.025). More than half of the cohort of patients had had a previous failure of cartilage-repair procedures. No significant differences were detected between younger patients (≤40 years) and older patients (>40 years). When compared with historical microfracture data, the BioPoly RS Implant demonstrated significantly superior KOOS scores for quality of life and sports., Conclusions: The present study indicated that the BioPoly RS Partial Resurfacing Knee Implant is safe, that it resulted in significantly improved knee function by 6 months, and that this improvement was sustained for 2 years regardless of patient age. The BioPoly RS Knee Implant allows return to a higher level of sporting activity than microfracture. Additional long-term follow-up is needed to determine the long-term effects of the device., Level of Evidence: Therapeutic Level IV. See Instructions for Authors for a complete description of levels of evidence.