Association between Cognitive Abilities before the Age of 3 and Those at Least 1 Year Later in Children with Developmental Delay.

AIM: This study investigated whether early cognitive assessment in children with developmental delay (DD) predicts cognitive development. We investigated the correlation between cognitive and language development in children with DD, cerebral palsy (CP), and autism spectrum disorder (ASD). METHODS: Data were collected from children diagnosed with DD who visited the hospital between 2015 and 2023. The assessments included the Korean Bayley Scales of Infant and Toddler Development Second Edition (K-BSID-II) and the Korean Wechsler Preschool Primary Scale of Intelligence Fourth Edition (K-WPPSI-IV). Language development was evaluated using the Sequenced Language Scale for Infants (SELSI) and Preschool Receptive-Expressive Language Scale (PRES). Correlation and multivariate regression analyses were performed. RESULTS: Among 95 children in the study, a significant correlation was discovered between early cognitive assessments (the Mental Developmental Index from the K-BSID-II) and later cognitive development (the Full-Scale Intelligence Quotient from the K-WPPSI-IV) in the DD and CP groups, but not in the ASD group. The DD and CP groups exhibited significant correlations in language development between the SELSI and PRES, whereas the ASD group did not. INTERPRETATION: Early cognitive assessments can predict later cognitive development in children with DD and CP, but not in those with ASD, according to this study. There was a strong correlation between language and cognitive development in the DD and CP groups, highlighting the importance of early intervention and assessment for these children. Further investigation is necessary to address these limitations and refine demographic data.

Noncontact Sensors for Vital Signs Measurement: A Narrative Review.

Vital signs are crucial for monitoring changes in patient health status. This review compared the performance of noncontact sensors with traditional methods for measuring vital signs and investigated the clinical feasibility of noncontact sensors for medical use. We searched the Medical Literature Analysis and Retrieval System Online (MEDLINE) database for articles published through September 30, 2023, and used the key search terms "vital sign," "monitoring," and "sensor" to identify relevant articles. We included studies that measured vital signs using traditional methods and noncontact sensors and excluded articles not written in English, case reports, reviews, and conference presentations. In total, 129 studies were identified, and eligible articles were selected based on their titles, abstracts, and full texts. Three articles were finally included in the review, and the types of noncontact sensors used in each selected study were an impulse radio ultrawideband radar, a microbend fiber-optic sensor, and a mat-type air pressure sensor. Participants included neonates in the neonatal intensive care unit, patients with sleep apnea, and patients with coronavirus disease. Their heart rate, respiratory rate, blood pressure, body temperature, and arterial oxygen saturation were measured. Studies have demonstrated that the performance of noncontact sensors is comparable to that of traditional methods of vital signs measurement. Noncontact sensors have the potential to alleviate concerns related to skin disorders associated with traditional skin-contact vital signs measurement methods, reduce the workload for healthcare providers, and enhance patient comfort. This article reviews the medical use of noncontact sensors for measuring vital signs and aimed to determine their potential clinical applicability.

Deep learning algorithm for automatically measuring Cobb angle in patients with idiopathic scoliosis.

PURPOSE: The Cobb angle is a standard measurement to qualify and track the progression of scoliosis. However, the Cobb angle has high inter- and intra-observer variability. Consequently, its measurement varies with vertebrae and may even differ when the same vertebra is measured. Therefore, it is not constant and differs with measurements. This study aimed to develop a deep learning model that automatically measures the Cobb angle. The deep learning model for identifying vertebrae on spine radiographs was developed. METHODS: The dataset consisted of 297 images that were divided into two subsets for training and validation. Two hundred and twenty-seven images (76.4%) were used to train the model, while 70 images (23.6%) were used as the validation dataset. Absolut error between the measurements by the observer and developed deep learning model and intraclass correlation coefficient (ICC). RESULTS: The average absolute error between the measurements was 1.97° with a standard deviation of 1.57°. In addition, 95.9% of the angles had an absolute error of less than 5°. The ICC was calculated to assess the model's reliability further. The ICC was 0.981, indicating excellent reliability. CONCLUSIONS: The authors believe the model will be useful in clinical practice by relieving clinicians of the burden of having to manually compute the Cobb angle. Further studies are needed to enhance the accuracy and versatility of this deep learning model.

Lacertus notch as a sign of lacertus syndrome.

OBJECTIVE: Many clinicians are unfamiliar with a diagnosis of lacertus syndrome (LS). We investigated the value of the lacertus notch sign in diagnosing LS. METHODS: We included 56 consecutive patients (112 upper extremities) who had neuropathic pain and neurological symptoms of the hand. The presence of LS and the lacertus notch sign in each upper extremity was assessed. RESULTS: Of the 83 upper extremities with LS, 54 (65.1%) had a lacertus notch sign, whereas 29 (34.9%) did not. Of the 29 upper extremities without LS, 9 (31.0%) and 20 (69.0%) had and did not have a lacertus notch sign, respectively. The rates of lacertus notch presence in upper extremities with and without LS were significantly different. Of the 63 upper extremities with a lacertus notch sign, 54 (85.7%) were diagnosed with LS, whereas 9 (14.3%) were not. Of the 49 upper extremities without a lacertus notch sign, 20 (40.8%) were diagnosed with LS, and 29 (59.2%) were not. We observed significant differences in the rates of LS in upper extremities with and without lacertus notch. CONCLUSIONS: The presence of the lacertus notch sign is useful for diagnosing LS. When patients with neuropathic pain and neurological symptoms present with a lacertus notch sign, clinicians should consider the possibility of LS.

Gadolinium enhancement in cervical dorsal roots in a patient with acute autonomic and sensory neuropathy: a case report.

BACKGROUND: We report an enhancement of the dorsal roots on gadolinium-enhanced cervical magnetic resonance imaging (MRI) in a patient with acute autonomic and sensory neuropathy (AASN). CASE PRESENTATION: A 38-year-old woman visited our university hospital for dizziness and fainting while rising from sitting or lying down and a tingling sensation in the whole body, including her limbs, torso, and abdomen, which was sustained for 15 days. The patient had hyperalgesia in nearly her entire body and slight motor weakness in her bilateral upper and lower limbs. Autonomic dysfunction was confirmed using autonomic testing. Furthermore, the nerve conduction study showed an absence of sensory nerve action potentials in all evaluated peripheral nerves. Cervical MRI was performed 18 days after dysautonomia onset. In the axial T1-gadolinum-enhanced MRIs, enhancement in cervical ventral and dorsal nerve roots and the posterior column of the spinal cord were observed, and the axial T2-weighted MRI showed high signal intensity in the posterior column of the cervical spinal cord. Considering the clinical, electrophysiological and imaging findings, the patient was diagnosed with AASN. A total dose of 90 g (2 g/kg) of intravenous immunoglobulin was administered over 5 days. At the follow-up at 4 years after AASN symptom onset, the hyperalgesia and orthostatic hypotension symptoms improved. However, her systolic blood pressure intermittently decreased to < 80 mmHg. CONCLUSION: Gadolinium-enhanced MRI may facilitate the accurate and prompt diagnosis of AASN.

The association between cognition and gait disturbance in central nervous system demyelinating disorder with mild disability.

INTRODUCTION: Gait disturbance in central nervous system (CNS) demyelinating disorders, including multiple sclerosis (MS) and neuromyelitis optica (NMO) is one of the most troublesome problems that has a direct impact on the quality of life. However, the associations between gait disturbance and other clinical variables of these two diseases have not been fully elucidated. OBJECTIVE: This study aimed to evaluate gait disturbance using a computerized gait analysis system and its association with various clinical variables in patients with MS and NMO. METHODS: A total of 33 patients (14 with MS and 19 with NMO) with minor disabilities, who were able to walk independently and had passed their acute phase, were enrolled in the study. Gait analysis were performed using a computer-based instrumented walkway system. (Walk-way MG-1000, Anima, Japan) Clinical variables, such as disease duration, medication, body mass index (BMI), hand grip power, and muscle mass were recorded. The Montreal Cognitive Assessment (MOCA), Beck Depression Inventory score-II (BDI), and fatigue scale were measured using the Functional Assessment of Chronic Illness Therapy-fatigue scale (FACIT-fatigue) scale. A trained neurologist scored the Expanded Disability Status Scale (EDSS). RESULTS: Gait speed was the single parameter that showed a significant positive correlation with MOCA (p < 0.001). The stance phase time was the single parameter that showed a significant negative correlation with EDSS (p < 0.001). Hand grip strength showed a significant positive correlation with skeletal muscle mass as assessed by bioimpedance analysis (p < 0.05). The FACIT-fatigue scale score showed a significant negative correlation with the BDI (p < 0.001). CONCLUSION: In our patients with MS/NMO with mild disability, cognitive impairment was significantly correlated with gait speed, and the degree of disability was significantly correlated with stance phase time. Our findings may imply that early detection of a decrease in gait speed and an increase in stance phase time can predict the progression of cognitive impairment in patients with MS/NMO with mild disability.

The effect of intensive rehabilitation treatment on sleep disorder in children with motor delays.

BACKGROUND: Although the importance of sleep problems has been increasingly emphasized due to the effects on children's development and children's and families' daytime behaviors, physical health, and quality of life, they have been overlooked in clinical practice. However, there have been few studies on the effects of rehabilitation on sleep problems. Therefore, in this study, we investigated the effects of an intensive rehabilitation program on sleep problems in children with developmental delays (DD). METHODS: We included 36 children with DD (30 outpatients, 6 inpatients) and their caregivers who completed all items on the Sleep Disturbance Scale for Children. Of the children with DD, 19 (59.3%) had cerebral palsy (CP) and 13 (40.7%) had DD of non-CP origins, of which 6 (18.8%) had prematurity, 4 (12.5%) had genetic causes, and 3 (9.4%) had an unknown origin. Changes in sleep problems after the intensive rehabilitation program were evaluated using a paired or unpaired t-test, depending on the distribution of the continuous variables. RESULTS: After the intensive rehabilitation program, in 36 children with DD, there was a significant improvement in the difficulty in initiating and maintaining sleep (DIMS) sub-score (p < 0.05). However, there was no significant improvement in the total score or other sub-scores, such as those for sleep breathing disorders (SBD), disorders of arousal (DA), sleep-wake transition disorders (SWTD), disorders of excessive somnolence (DOES), and sleep hyperhidrosis (SH). In the subgroup analysis according to the cause of DD, children with CP had a significant improvement in DIMS and DOES sub-scores (p < 0.05). CONCLUSION: The intensive rehabilitation program, consisting of more than two sessions per day, effectively alleviated sleep problems in children with DD, especially in those with CP. Among the sleep problems, the intensive rehabilitative program was most effective at improving the DIMS. However, further prospective studies with a larger number of patients with DD and a more standardized protocol are necessary to generalize this effect.

Effect of Repetitive Transcranial Magnetic Stimulation on Post-stroke Dysphagia: A Meta-analysis of Stimulation Frequency, Stimulation Site, and Timing of Outcome Measurement.

Dysphagia is one of the most frequent sequelae of stroke. It can result in various complications such as malnutrition, dehydration, aspiration pneumonia, and poor rehabilitation outcomes. Repetitive transcranial magnetic stimulation (rTMS) has been reported to improve dysphagia after a stroke; however, effective treatment protocols have not been established yet. We evaluated the effect of the following rTMS parameters on post-stroke dysphagia: stimulation frequency [high frequency (≥ 3 Hz) or low frequency (1 Hz)] and stimulation site (ipsilesional or contralesional mylohyoid cortex). Outcomes were measured immediately, at 3 weeks, and at 4 weeks after the rTMS session. The PubMed, SCOPUS, Embase, and Cochrane Library databases were systematically searched for relevant studies published between January 01, 1980, and December 13, 2021. Randomized controlled trials on the effects of rTMS on post-stroke dysphagia were included. Six studies were finally included in the analysis. The selected studies included 158 patients (rTMS group: 81 patients; sham group: 77 patients). Regarding the effect of high-frequency rTMS on the ipsilesional cortex, the standardized swallowing assessment (SSA) scores showed significant improvement after rTMS sessions immediately and at 4 weeks [immediate: P = 0.02, standard mean difference (SMD) = - 0.61, 95% confidence interval (CI) = - 1.14 to - 0.08; 4 weeks: P = 0.006, SMD = - 0.74, 95% CI = - 1.27 to - 0.21]; however, there was no significant reduction in the Penetration-Aspiration Scale (PAS) scores between the rTMS and sham groups (immediate: P = 0.43, SMD = 0.25, 95% CI = - 0.36, 0.86; 3 weeks: P = 0.39, SMD = 0.37, 95% CI = - 0.47 to 1.22). After low-frequency rTMS on the ipsilesional cortex, a significantly greater improvement in the SSA scores was found in the rTMS group than in the sham group, both immediately and at 4 weeks after rTMS sessions (immediate: P = 0.03, SMD = - 0.59, 95% CI = - 1.12 to - 0.06; 4 weeks: P = 0.001, SMD = - 0.92, 95% CI = - 1.48 to - 0.37). In addition, immediately after the rTMS sessions, the PAS scores were significantly reduced in the rTMS group than in the sham group (P = 0.047, SMD = - 0.60, 95% CI = - 1.19 to - 0.01). However, at 4 weeks after rTMS sessions, there was no significant reduction in the PAS scores in the rTMS group compared to the sham group (P = 0.48, SMD = - 0.19, 95% CI = - 0.71 to 0.33). Both high-frequency rTMS of the ipsilesional cortex and low-frequency rTMS of the contralesional cortex improved some measurements of the swallowing function in stroke patients immediately and at 4 weeks after treatment.

Prediction of motor outcome based on brain perfusion single photon emission computed tomography in corona radiata infarct.

BACKGROUND: There is limited information on brain perfusion single-photon emission computed tomography (SPECT) findings related to motor outcomes in patients with stroke. We aimed to investigate whether brain SPECT can be used to determine motor outcomes after corona radiata infarction. METHODS: Eighty-nine patients were recruited in this study. Brain SPECT and diffusion tensor tractography (DTT) were conducted to evaluate the state of the corticospinal tract (CST) within 7-30 days of corona radiata infarct. Motor outcome was measured 6 months after infarct onset and was evaluated using the modified Brunnstrom classification (MBC) and functional ambulation category (FAC) for motor function of the upper and lower extremities, respectively. The presence of hypoperfusion on brain SPECT was evaluated in the frontal lobe, temporal lobe, parietal lobe, basal ganglia, thalamus, and cerebellum on both the ipsilesional and contralesional sides. Statistical analysis was performed using multivariate logistic regression, comparing patients in which CST was spared versus interrupted. RESULTS: Hypoperfusion in the contralesional cerebellum was indicative of poor recovery in both the upper and lower extremities after corona radiata infarction when the CST was interrupted. Additionally, when the CST was preserved, hypoperfusion in the ipsilesional thalamus was indicative of poor recovery of the lower extremities. CONCLUSION: Brain SPECT evaluation was shown to be a useful tool for predicting motor outcomes in patients with corona radiata infarcts.

Deep learning algorithm to evaluate cervical spondylotic myelopathy using lateral cervical spine radiograph.

BACKGROUND: Deep learning (DL) is an advanced machine learning approach used in different areas such as image analysis, bioinformatics, and natural language processing. A convolutional neural network (CNN) is a representative DL model that is highly advantageous for imaging recognition and classification This study aimed to develop a CNN using lateral cervical spine radiograph to detect cervical spondylotic myelopathy (CSM). METHODS: We retrospectively recruited 207 patients who visited the spine center of a university hospital. Of them, 96 had CSM (CSM patients) while 111 did not have CSM (non-CSM patients). CNN algorithm was used to detect cervical spondylotic myelopathy. Of the included patients, 70% (145 images) were assigned randomly to the training set, while the remaining 30% (62 images) to the test set to measure the model performance. RESULTS: The accuracy of detecting CSM was 87.1%, and the area under the curve was 0.864 (95% CI, 0.780-0.949). CONCLUSION: The CNN model using the lateral cervical spine radiographs of each patient could be helpful in the diagnosis of CSM.

Cytomegalovirus lumbosacral polyradiculitis in patients with long-term use of an oral corticosteroid: a case report.

BACKGROUND: The long-term use of an oral corticosteroid suppresses immunity. Here, we describe a case involving a patient with weakness in the bilateral lower extremities due to cytomegalovirus (CMV) lumbosacral polyradiculitis. CASE PRESENTATION: A 64-year-old man visited a university hospital for symmetric motor weakness in both lower extremities (Medical Research Council grade: 2). Symptoms started 1 month before and gradually aggravated. The patient had been taking oral prednisolone for 10 years in order to control pain in multiple joints due to seronegative rheumatoid arthritis. He also had neuropathic pain on the entire right lower extremity and voiding difficulty. Gadolinium-enhanced magnetic resonance imaging revealed enhancement along the entire lumbosacral nerve roots. In the cerebrospinal fluid analysis (CSF), elevated white blood cell (WBC) count (19 cells/µL) and protein level (142.5 mg/dL) were observed. CMV detection by polymerase chain reaction (PCR) was positive. We diagnosed the patient as having lumbosacral polyradiculitis due to CMV. Ganciclovir (250 mg twice daily) was administered intravenously. Two months after initiating Ganciclovir, in the CSF analysis, CM detection by PCR was negative, and no WBC was found. CONCLUSION: We reported a patient who had symmetric motor weakness in the bilateral lower extremities induced by CMV lumbosacral polyradiculitis. Its occurrence seems to be related to immunosuppresion due to the long-term use of an oral corticosteroid. When a patient who is taking an oral corticosteroid shows motor weakness in the bilateral lower extremities, CMV lumbosacral polyradiculitis is one of the possible disorders to be differentiated.

Effectiveness of Repetitive Transcranial Magnetic Stimulation on Managing Fibromyalgia: A Systematic Meta-Analysis.

OBJECTIVE: In fibromyalgia, central sensitization is a key mechanism, and repetitive transcranial magnetic stimulation (rTMS) has been reported to potentially manage symptoms of fibromyalgia. In this meta-analysis, we evaluated the therapeutic effect of rTMS in patients with fibromyalgia according to stimulation locations and follow-up time points. METHODS: We searched the MEDLINE, Cochrane, Embase, Scopus, Cumulative Index to Nursing and Allied Health Literature, and Web of Science databases for articles published from January 1, 1990, to August 26, 2021, including randomized controlled studies investigating the effectiveness of rTMS on managing fibromyalgia. RESULTS: In total, 10 articles and 299 participants were included. High-frequency rTMS on the left primary motor cortex (Lt. M1) had a significant effect on pain reduction immediately and 1-4 weeks after the end of the session but had no significant effect after 5-12 weeks. Additionally, after high-frequency rTMS sessions on the Lt. M1, the effect on patients' quality of life appeared late at 5-12 weeks of follow-up. In contrast, high-frequency rTMS on the left dorsolateral prefrontal cortex (Lt. DLPFC) did not reduce pain from fibromyalgia. The effect on controlling the affective problem was not observed after rTMS treatment on either the Lt. M1 or the Lt. DLPFC. CONCLUSIONS: High-frequency rTMS had a positive pain-reducing effect immediately and at 1-4 weeks after completion of the rTMS sessions, and the patients' quality of life improved after 5-12 weeks. However, Lt. DLPFC stimulation was not effective in controlling fibromyalgia symptoms.

Practical Machine Learning Model to Predict the Recovery of Motor Function in Patients with Stroke.

BACKGROUND: Machine learning (ML) is an artificial intelligence technique in which a system learns patterns and rules from a given data. OBJECTIVES: The objective of the study was to investigate the potential of ML for predicting motor recovery in stroke patients. METHODS: We analyzed data from 833 consecutive stroke patients using 3 ML algorithms: deep neural network (DNN), random forest, and logistic regression. We created a practical ML model using the most common data measured in almost all rehabilitation hospitals as input data. Demographic and clinical data, including modified Brunnstrom classification (MBC) and functional ambulation classification (FAC), were collected when patients were transferred to the rehabilitation unit (8-30 days) and 6 months after stroke onset and were used as input data. Motor outcomes at 6 months after stroke onset of the affected upper and lower extremities were classified according to MBC and FAC, respectively. Patients with an MBC of <5 and an FAC of <4 at 6 months after stroke onset were considered to have a "poor" outcome, whereas those with MBC ≥5 and FAC ≥4 were considered to have a "good" outcome. RESULTS: The area under the curve (AUC) for the DNN model for predicting motor function was 0.836 for the upper and lower limb motor functions. For the random forest and logistic regression models, the AUCs were 0.736 and 0.790 for the upper and lower limb motor functions, respectively. The AUCs for the random forest and logistic regression models were 0.741 and 0.795 for the upper and lower limb motor functions, respectively. CONCLUSION: Although we used simple and common data that can be obtained in clinical practice as variables, our DNN algorithm was useful for predicting motor recovery of the upper and lower extremities in stroke patients during the recovery phase.

Prediction of Motor Outcome of Stroke Patients Using a Deep Learning Algorithm with Brain MRI as Input Data.

BACKGROUND: Deep learning techniques can outperform traditional machine learning techniques and learn from unstructured and perceptual data, such as images and languages. We evaluated whether a convolutional neural network (CNN) model using whole axial brain T2-weighted magnetic resonance (MR) images as input data can help predict motor outcomes of the upper and lower limbs at the chronic stage in stroke patients. METHODS: We collected MR images taken at the early stage of stroke in 1,233 consecutive stroke patients. We categorized modified Brunnstrom classification (MBC) scores of ≥5 and functional ambulatory category (FAC) scores of ≥4 at 6 months after stroke as favorable outcomes in the upper and lower limbs, respectively, and MBC scores of <5 and FAC scores of <4 as poor outcomes. We applied a CNN to train the image data. Of the 1,233 patients, 70% (863 patients) were randomly selected for the training set and the remaining 30% (370 patients) were assigned to the validation set. RESULTS: In the prediction of upper limb motor function on the validation dataset, the area under the curve (AUC) was 0.768, and for lower limb motor function, the AUC was 0.828. CONCLUSION: We showed that a CNN model trained using whole-brain axial T2-weighted MR images of stroke patients would help predict upper and lower limb motor function at the chronic stage.

Development of convolutional neural network model for diagnosing meniscus tear using magnetic resonance image.

BACKGROUND: Deep learning (DL) is an advanced machine learning approach used in diverse areas, such as image analysis, bioinformatics, and natural language processing. A convolutional neural network (CNN) is a representative DL model that is advantageous for image recognition and classification. In this study, we aimed to develop a CNN to detect meniscal tears and classify tear types using coronal and sagittal magnetic resonance (MR) images of each patient. METHODS: We retrospectively collected 599 cases (medial meniscus tear = 384, lateral meniscus tear = 167, and medial and lateral meniscus tear = 48) of knee MR images from patients with meniscal tears and 449 cases of knee MR images from patients without meniscal tears. To develop the DL model for evaluating the presence of meniscal tears, all the collected knee MR images of 1048 cases were used. To develop the DL model for evaluating the type of meniscal tear, 538 cases with meniscal tears (horizontal tear = 268, complex tear = 147, radial tear = 48, and longitudinal tear = 75) and 449 cases without meniscal tears were used. Additionally, a CNN algorithm was used. To measure the model's performance, 70% of the included data were randomly assigned to the training set, and the remaining 30% were assigned to the test set. RESULTS: The area under the curves (AUCs) of our model were 0.889, 0.817, and 0.924 for medial meniscal tears, lateral meniscal tears, and medial and lateral meniscal tears, respectively. The AUCs of the horizontal, complex, radial, and longitudinal tears were 0.761, 0.850, 0.601, and 0.858, respectively. CONCLUSION: Our study showed that the CNN model has the potential to be used in diagnosing the presence of meniscal tears and differentiating the types of meniscal tears.

Deep Learning Analysis to Automatically Detect the Presence of Penetration or Aspiration in Videofluoroscopic Swallowing Study.

BACKGROUND: Videofluoroscopic swallowing study (VFSS) is currently considered the gold standard to precisely diagnose and quantitatively investigate dysphagia. However, VFSS interpretation is complex and requires consideration of several factors. Therefore, considering the expected impact on dysphagia management, this study aimed to apply deep learning to detect the presence of penetration or aspiration in VFSS of patients with dysphagia automatically. METHODS: The VFSS data of 190 participants with dysphagia were collected. A total of 10 frame images from one swallowing process were selected (five high-peak images and five low-peak images) for the application of deep learning in a VFSS video of a patient with dysphagia. We applied a convolutional neural network (CNN) for deep learning using the Python programming language. For the classification of VFSS findings (normal swallowing, penetration, and aspiration), the classification was determined in both high-peak and low-peak images. Thereafter, the two classifications determined through high-peak and low-peak images were integrated into a final classification. RESULTS: The area under the curve (AUC) for the validation dataset of the VFSS image for the CNN model was 0.942 for normal findings, 0.878 for penetration, and 1.000 for aspiration. The macro average AUC was 0.940 and micro average AUC was 0.961. CONCLUSION: This study demonstrated that deep learning algorithms, particularly the CNN, could be applied for detecting the presence of penetration and aspiration in VFSS of patients with dysphagia.

Neuropathic pain due to compression of cervical medial branch by cervical Juxtafacet cyst: A case report.

In the current report, we describe an interesting case of cervical juxtafacet that developed outside the intervertebral foramen, compressing the cervical medial branch and causing neuropathic pain in the posterior inferior neck pain. A 61-year-old woman visited a local pain clinic due to neuropathic pain with a tingling and burning nature (numeric rating scale [NRS]: 5 out of 10) on the left posterior inferior neck area for 4 months. Paresthesia was observed in the left posterior inferior neck area. On cervical radiography, segmental instability was observed at the C3-4 and C4-5 levels. Moreover, on the magnetic resonance imaging (MRI) of the cervical spine, a cyst (size: 1.3 cm × 0.7 cm × 1 cm) was outside the intervertebral foramen, contacting the left C4-5 facet joint and left C5 articular pillar. We thought that the compression of the left C5 medial branch by the cyst could cause the patient's pain. We conducted computed tomography (CT)-guided percutaneous needle aspiration of a cervical juxtafacet cyst. An 18-gauge needle was advanced under the guidance of CT into the largest portion of the cyst through a posterolateral oblique approach. Gelatinous mucoid fluid (approximately 0.5 cc) was aspirated. Immediately after the aspiration, 80% of the patient's pain was disappeared, and dysesthesia was completely disappeared. At the 1-, 3-, and 6-month follow-ups, the patient reported slight pain (NRS: 1) on the left posterior inferior neck. Cervical juxtafacet cysts can develop outside of the intervertebral foramen and spinal canal. Percutaneous needle aspiration can be a useful therapeutic tool for the treatment of such cysts.

Effectiveness of prednisolone in complex regional pain syndrome treatment: A systematic narrative review.

BACKGROUND: Many patients with complex regional pain syndrome (CRPS) experience refractory pain with severe restrictions in the activities of daily living. Oral prednisolone is commonly used to treat these patients. PURPOSE: To review previous studies assessing the effects of prednisolone in CRPS patients. METHODS AND MATERIALS: Articles published from January 1, 1980 to July 23, 2021 in the PubMed database were searched using the following key phrases: (prednisolone OR corticosteroid OR steroid) AND (complex regional pain syndrome OR reflex sympathetic dystrophy OR shoulder-hand syndrome OR causalgia). Specifically, we included those articles in which oral prednisolone or corticosteroids were used to control the CRPS symptoms. RESULTS: In total, 11 articles were included, comprising 3 randomized trials, 5 single-arm prospective observational studies, and 3 retrospective studies. Nearly all previous studies reported that oral prednisolone can effectively control the CRPS symptoms. Moreover, though 30-100 mg/day of oral prednisolone was initially administered in these studies, 30 mg/day was also found to be effective in controlling the symptoms. Although prednisolone was usually administered for 1-3 months, short-term treatment for 1-2 weeks was also reportedly effective. Furthermore, only 0%-30% of the patients in these studies had minor side effects after prednisolone treatment. CONCLUSIONS: Our review showed that prednisolone may be effective in alleviating the CRPS symptoms. To determine higher levels of evidence, a full systematic review with more highly qualified studies, such as randomized controlled trials, should be conducted in the future.

Poststroke Pain.

Pain is common but often underrecognized after stroke. Poststroke pain (PSP) hinders recovery, impairs quality of life, and is associated with the psychological state of patients with stroke. The most common subtypes of PSP include central PSP, complex regional pain syndrome, shoulder pain, spasticity-related pain, and headache. The pathophysiologies of these PSP subtypes are not yet clearly understood, and PSP is refractory to conventional treatment in many patients. However, recent studies have proposed potential pathophysiologies of PSP subtypes, which may help prioritize therapies that target specific mechanisms.

The parameters of gait analysis related to ambulatory and balance functions in hemiplegic stroke patients: a gait analysis study.

BACKGROUND: Ambulatory and balance functions are important for maintaining general health in humans. Gait analysis allows clinicians and researchers to identify the parameters to be focused on when assessing balance and ambulatory functions. In this study, we performed gait analysis with pressure sensors to identify the gait-analysis parameters related to balance and ambulatory functions in hemiplegic stroke patients. METHODS: We retrospectively reviewed the medical records of 102 patients with hemiplegic stroke who underwent gait analysis. Correlations between various temporospatial parameters in the gait analysis and the motor and balance functions assessed using functional ambulation category, modified Barthel index, and Berg balance scale were analyzed. RESULTS: Gait speed/height and the lower-limb stance-phase time/height were the only temporal and spatial parameters, respectively, that showed a statistical correlation with motor and balance functions. CONCLUSIONS: Measurements of walking speed and stance-phase time of the unaffected lower limb can allow clinicians to easily assess the ambulatory and balance functions of hemiplegic stroke patients. Rehabilitative treatment focusing on increasing gait speed and shortening the stance-phase time of the unaffected side may improve the ambulatory and balance functions in these patients.