Objectives The aim of the present study was to explore the feasibility of ultrasonography (US) for clinical imaging of peri‐implant tissues. Material and Methods Patients with ≥1 implant, a cone‐beam computed tomography (CBCT) scan, an US scan, and clinical photographs taken during the surgery were included. The crestal bone thickness (CBT) and facial bone level (FBL) were measured on both US and CBCT modalities, and direct FBL measurements were also made on clinical images. US measurements were compared with CBCT and direct readings. Results A total of eight implants from four patients were included. For FBL measurements, US and direct (r2 = 0.95) as well as US and CBCT (r2 = 0.85) were highly correlated, whereas CBCT correlated satisfactorily with the direct reading (r2 = 0.75). In one implant without facial bone, CBCT was not able to measure CBT and FBL accurately. The estimated bias for CBT readings was 0.17 ± 0.23 mm (p = .10) between US and CBCT. US blood flow imaging was successfully recorded and showed a wide dynamic range among patients with different degrees of clinical inflammation. Conclusion US is a feasible method to evaluate peri‐implant facial crestal bone dimensions. Additional US features, for example, functional blood flow imaging, may be useful to estimate the extent and severity of inflammation.
Background: Accelerometers have become common for evaluating the efficacy of rehabilitation for patients with neurologic disorders. For example, metrics like use ratio (UR) and magnitude ratio (MR) have been shown to differentiate movement patterns of children with cerebral palsy (CP) compared to typically-developing (TD) peers. However, these metrics are calculated from "activity counts"a measure based on proprietary algorithms that approximate movement duration and intensity from raw accelerometer data. Algorithms used to calculate activity counts vary between devices, limiting comparisons of clinical and research results. The goal of this research was to develop complementary metrics based on raw accelerometer data to analyze arm movement after neurologic injury. Method: We calculated jerk, the derivative of acceleration, to evaluate arm movement from accelerometer data. To complement current measures, we calculated jerk ratio (JR) as the relative jerk magnitude of the dominant (nonparetic) and non-dominant (paretic) arms. We evaluated the JR distribution between arms and calculated the 50th percentile of the JR distribution (JR50). To evaluate these metrics, we analyzed bimanual accelerometry data for five children with hemiplegic CP who underwent Constraint-Induced Movement Therapy (CIMT) and five typically developing (TD) children. We compared JR between the CP and TD cohorts, and to activity count metrics. Results: The JR50 differentiated between the CP and TD cohorts (CP = 0.578 ± 0.041 before CIMT, TD = 0.506 ± 0.026), demonstrating increased reliance on the dominant arm for the CP cohort. Jerk metrics also quantified changes in arm use during and after therapy (e.g., JR50 = 0.378 ± 0.125 during CIMT, 0.591 ± 0.057 after CIMT). The JR was strongly correlated with UR and MR (r = − 0.92, 0.89) for the CP cohort. For the TD cohort, JR50 was repeatable across three data collection periods with an average similarity of 0.945 ± 0.015. Conclusions: Acceleration-derived jerk captured differences in motion between TD and CP cohorts and correlated with activity count metrics. The code for calculating and plotting JR is open-source and available for others to use and build upon. By identifying device-independent metrics that can quantify arm movement in daily life, we hope to facilitate collaboration for rehabilitation research using wearable technologies.
Importance: Constraint-induced movement therapy (CIMT) is a common treatment for children with unilateral cerebral palsy (CP). Although clinic-based assessments have demonstrated improvements in arm function after CIMT, whether these changes are translated and sustained outside of a clinic setting remains unclear. Objective: Accelerometers were used to quantify arm movement for children with CP 1 wk before, during, and 4 wk or more after CIMT; measurements were compared with those from typically developing (TD) peers. Design: Observational. Setting: Tertiary hospital and community. Participants: Seven children with CP (5 boys, 2 girls; average [AVE] age ± standard deviation [SD] = 7.4 ± 1.2 yr) and 7 TD peers (2 boys, 5 girls; AVE age ± SD = 7.0 ± 2.3 yr). Intervention: 30-hr CIMT protocol. Outcomes and Measures: Use ratio, magnitude ratio, and bilateral magnitude were calculated from the accelerometer data. Clinical measures were administered before and after CIMT, and parent surveys assessed parent and child perceptions of wearing accelerometers. Results: During CIMT, the frequency and magnitude of paretic arm use among children with CP increased in the clinic and in daily life. After CIMT, although clinical scores showed sustained improvement, the children’s accelerometry data reverted to baseline values. Children and parents in both cohorts had positive perceptions of accelerometer use. Conclusions and Relevance: The lack of sustained improvement in accelerometry metrics after CIMT suggests that therapy gains did not translate to increased movement outside the clinic. Additional therapy may be needed to help transfer gains outside the clinic. What This Article Adds: Accelerometer measurements were effective at monitoring arm movement outside of the clinic during CIMT and suggested that additional interventions may be needed after CIMT to sustain benefits.
Objectives: Ultrasound emerges as a complement to cone-beam computed tomography in dentistry, but struggles with artifacts like reverberation and shadowing. This study seeks to help novice users recognize soft tissue, bone, and crown of a dental sonogram, and automate soft tissue height (STH) measurement using deep learning. Methods: In this retrospective study, 627 frames from 111 independent cine loops of mandibular and maxillary premolar and incisors collected from our porcine model (N = 8) were labeled by a reader. 274 premolar sonograms, including data augmentation, were used to train a multi class segmentation model. The model was evaluated against several test sets, including premolar of the same breed (n = 74, Yucatan) and premolar of a different breed (n = 120, Sinclair). We further proposed a rule-based algorithm to automate STH measurements using predicted segmentation masks. Results: The model reached a Dice similarity coefficient of 90.7±4.39%, 89.4±4.63%, and 83.7±10.5% for soft tissue, bone, and crown segmentation, respectively on the first test set (n = 74), and 90.0±7.16%, 78.6±13.2%, and 62.6±17.7% on the second test set (n = 120). The automated STH measurements have a mean difference (95% confidence interval) of −0.22 mm (−1.4, 0.95), a limit of agreement of 1.2 mm, and a minimum ICC of 0.915 (0.857, 0.948) when compared to expert annotation. Conclusion: This work demonstrates the potential use of deep learning in identifying periodontal structures on sonograms and obtaining diagnostic periodontal dimensions.
Importance: Constraint Induced Movement Therapy (CIMT) is a common treatment for children with unilateral cerebral palsy (CP). While clinic-based assessments have demonstrated improvements in arm function after CIMT, quantifying if these changes are translated and sustained outside of a clinic setting remains unclear.Objective: Accelerometers were used to quantify arm movement for children with CP one week before, during, and 4+ weeks after CIMT and compared to typically-developing (TD) peers. Design: Observational during CIMTSetting: Clinical assessments and treatment occurred in a tertiary hospital and accelerometry data were collected in the community Participants: 7 children with CP (5m/2f, 7.4 ± 1.2 yrs) and 7 TD peers (2m/5f, 7.0 ± 2.3 yrs) Intervention: 30-hour CIMT protocolOutcomes and Measures: The use ratio, magnitude ratio, and bilateral magnitude were calculated from the accelerometry data. Clinical measures were evaluated before and after CIMT and surveys were used to assess the feasibility of using accelerometers.Results: Before CIMT, children with CP used their paretic arm less than their TD peers. During therapy, their frequency and magnitude of paretic arm use increased in the clinic and in daily life.After therapy, although clinical scores improved, children reverted to baseline accelerometry values. Additionally, children and parents in both cohorts had positive perceptions of wearing accelerometers. Conclusions and Relevance:The lack of sustained improved accelerometry metrics following CIMT suggest therapy gains did not translate to increased movement outside the clinic.Additional therapy may be needed to help the transfer of skills to the community setting.What this Article Adds: This study compares the movement of children with CP undergoing CIMT in the community setting with their typically developing peers. Additional interventions may be needed in combination with or following CIMT to sustain the benefits of the therapy outside of the clinic.
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