Query: hammersmith infant neurological examination

title: Autonomous vision guided approach for the analysis and grading of vertical suspension tests during Hammersmith Infant Neurological Examination (HINE)
abstract: Computer vision assisted diagnostic systems are gaining popularity in different healthcare applications. This paper presents a video analysis and pattern recognition framework for the automatic grading of vertical suspension tests on infants during the Hammersmith Infant Neurological Examination (HINE) The proposed vision guided pipeline applies a color based skin region segmentation procedure followed by the localization of body parts before feature extraction and classification. After constrained localization of lower body parts, a stick diagram representation is used for extracting novel features that correspond to the motion dynamic characteristics of the infant's leg movements during HINE. This set of pose features generated from such a representation includes knee angles and distances between knees and hills. Finally, a time series representation of the feature vector is used to train a Hidden Markov Model (HMM) for classifying the grades of the HINE tests into three predefined categories. Experiments are carried out by testing the proposed framework on a large number of vertical suspension test videos recorded at a Neuro development clinic. The automatic grading results obtained from the proposed method matches the scores of experts at an accuracy of 74%

title: Exercise classification and event segmentation in Hammersmith Infant Neurological Examination videos
abstract: Image and video processing techniques are being frequently used in medical science applications. Computer vision based systems have successfully replaced various manual medical processes such as analyzing physical and biomechanical parameters, physical examination of patients. These systems are gaining popularity because of their robustness and the objectivity they bring to various medical procedures. Hammersmith Infant Neurological Examinations (HINE) is a set of physical tests that are carried out on infants in the age group of 3 24 months with neurological disorders. However, these tests are graded through visual observations, which can be highly subjective. Therefore, computer vision aided approach can be used to assist the experts in the grading process. In this paper, we present a method of automatic exercise classification through visual analysis of the HINE videos recorded at hospitals. We have used scale invariant feature transform features to generate a bag of words from the image frames of the video sequences. Frequency of these visual words is then used to classify the video sequences using HMM. We also present a method of event segmentation in long videos containing more than two exercises. Event segmentation coupled with a classifier can help in automatic indexing of long and continuous video sequences of the HINE set. Our proposed framework is a step forward in the process of automation of HINE tests through computer vision based methods. We conducted tests on a dataset comprising of 70 HINE video sequences. It has been found that the proposed method can successfully classify exercises with accuracy as high as 84% The proposed work has direct applications in automatic or semiautomatic analysis of "vertical suspension" and "ventral suspension" tests of HINE. Though some of the critical tests such as "pulled to sit," "lateral tilting," or "adductor's angle measurement" have already been addressed using image and video guided techniques, scopes are there for further improvement.

title: A Tool for Automatic Hammersmith Infant Neurological Examination
abstract: Hammersmith Infant Neurological Examination HINE is a popular method to estimate the neurological development of infants aged less than two years. Using HINE, especially for preterm or premature ba.

title: Hammersmith Infant Neurological Examination neurologisen tutkimusmenetelman kaantaminen suomeksi

title: Analysis of adductors angle measurement in Hammersmith infant neurological examinations using mean shift segmentation and feature point based object tracking
abstract: This paper presents image and video analysis based schemes to automate the process of adductors angle measurement which is carried out on infants as a part of Hammersmith Infant Neurological Examination (HINE) Image segmentation, thinning and feature point based object tracking are used for automating the analysis. Segmentation outputs are processed with a novel region merging algorithm. It is found that the refined segmentation outputs can successfully be used to extract features in the context of the application under consideration. Next, a heuristic based filtering algorithm is applied on the thinned structures for locating necessary points to measure adductors angle. A semi automatic scheme based on the object tracking of a video has been proposed to minimize errors of the image based analysis. It is observed that the video based analysis outperforms the image based method. A fully automatic method has also been proposed and compared with the semi automatic algorithm. The proposed methods have been tested with several videos recorded from hospitals and the results have been found to be satisfactory in the present context.

title: Infant neurological examination from 3 to 12 months: predictive value of the single items.
abstract: The prognostic value of the single items of a standardised neurological examination, the Hammersmith Infant Neurologic Examination (HINE) was explored longitudinally in 658 infants at 3, 6, 9 and 12 months post term age. ROC curves were built based on the presence/absence of cerebral palsy at 2 years of age. Global HINE scores showed very high prediction (ROC curve areas above 0.9) at all ages. The items with the highest predictive value were always movement quality and quantity. In the first semester, among the most predictive items were those assessing tone, while beyond that time they were reflexes and reactions. Our results show that the high predictive value of the HINE across the first year of life is granted by the successful combination of different groups of items for each age period. This should be recognised in clinical practice when assessing the significance of individual neurological profiles.

title: The Pooled Diagnostic Accuracy of Neuroimaging, General Movements, and Neurological Examination for Diagnosing Cerebral Palsy Early in High Risk Infants: A Case Control Study
abstract: Clinical guidelines recommend using neuroimaging, Prechtls' General Movements Assessment (GMA) and Hammersmith Infant Neurological Examination (HINE) to diagnose cerebral palsy (CP) in infancy. Previous studies provided excellent sensitivity and specificity for each test in isolation, but no study has examined the pooled predictive power for early diagnosis.  We performed a retrospective case control study of 441 high risk infants born between 2003 and 2014, from three Italian hospitals. Infants with either a normal outcome, mild disability, or CP at two years, were matched for birth year, gender, and gestational age. Three month HINE, GMA, and neuroimaging were retrieved from medical records. Logistic regression was conducted with log likelihood and used to determine the model fit and Area Under the Curve (AUC) for accuracy.  Sensitivity and specificity for detecting CP were 88% and 62% for three month HINE, 95% and 97% for absent fidgety GMs, and 79% and 99% for neuroimaging. The combined predictive power of all three assessments gave sensitivity and specificity values of 97.86% and 99.22% (PPV 98.56% NPV 98.84%  CP can be accurately detected in high risk infants when these test findings triangulate. Clinical implementation of these tools is likely to reduce the average age when CP is diagnosed, and intervention is started.

title: Neonatal neurological examination during the first 6h after birth.
abstract: Few neurological assessments are easily performed during the first 6h after birth.  To assess a cohort of low risk term born newborns within the first 6h and at 48h after birth using the Hammersmith Neonatal Neurological Examination.  A population of low risk term born newborns was examined within 3h from birth or between 3 and 6h. Each infant was re assessed at 48h, establishing the range and frequency distribution of neonatal neurological scores at each time point.  Of the 124 full term born newborns, 62 were assessed at 0 3h and 62 at 3 6h. All infants were re assessed at 48h. For 23/34 of the neurological items, the range and median scores were similar across the 3 time points. In the remaining 11 items the three groups had a similar range of scores but the median scores were different with different rates of changes. In 6 of the 11 the median scores at 3 6h were similar to those observed at 48h but they appeared to be 'less mature' at 0 3h. Only in one item the median scores were consistently different at the 3 time points.  Our results suggest that a neurological examination can already be reliably performed soon after birth. These findings will help in the interpretation of the few items that show changes with increasing postnatal age.

title: Neurological examination combined with brain MRI or cranial US improves prediction of neurological outcome in preterm infants.
abstract: The predictive value of the combination of neurological examination and brain magnetic resonance imaging (MRI) or cranial ultrasound (cUS) in preterm infants is not known.  To study the prognostic value of the combination of neurological examination and brain MRI at term equivalent age (TEA) or serial neonatal cUS in very preterm infants for neurosensory outcome at 2 years of corrected age.  A prospective follow up study.  A total of 216 very preterm infants (birth weight 1132 g [SD 331 g] born in Turku University Hospital, from 2001 to 2006, were included.  The Dubowitz neurologic examination and brain MRI were done at TEA, and serial cUS examinations were performed until TEA. The Hammersmith Infant Neurological Examination (HINE) and neurosensory impairments (NSI) were assessed at 2 years of corrected age.  Of all infants, 163 (76% had one or more deviant neurological items at TEA, and 32 (15% had the HINE total score below the 10th percentile at 2 years of corrected age. A total of 17 (8% infants had NSI. Neurological examination at TEA improved the negative and positive predictive values of brain MRI for NSI from 99% to 100% and from 28% to 35% respectively, and the negative and positive predictive values of cUS from 97% to 100% and from 61% to 79% respectively.  The combination of the Dubowitz neurologic examination and the brain MRI at TEA or serial neonatal cUS provides a valuable clinical tool for predicting long term neurosensory outcome in preterm infants.

title: Toward Automating Hammersmith Pulled To Sit Examination of Infants Using Feature Point Based Video Object Tracking
abstract: Hammersmith Infant Neurological Examination (HINE) is a set of tests used for grading neurological development of infants on a scale of 0 to 3. These tests help in assessing neurophysiological development of babies, especially preterm infants who are born before (the fetus reaches) the gestational age of 36 weeks. Such tests are often conducted in the follow up clinics of hospitals for grading infants with suspected disabilities. Assessment based on HINE depends on the expertise of the physicians involved in conducting the examinations. It has been noted that some of these tests, especially pulled to sit and lateral tilting, are difficult to assess solely based on visual observation. For example, during the pulled to sit examination, the examiner needs to observe the relative movement of the head with respect to torso while pulling the infant by holding wrists. The examiner may find it difficult to follow the head movement from the coronal view. Video object tracking based automatic or semi automatic analysis can be helpful in this case. In this paper, we present a video based method to automate the analysis of pulled to sit examination. In this context, a dynamic programming and node pruning based efficient video object tracking algorithm has been proposed. Pulled to sit event detection is handled by the proposed tracking algorithm that uses a 2 D geometric model of the scene. The algorithm has been tested with normal as well as marker based videos of the examination recorded at the neuro development clinic of the SSKM Hospital, Kolkata, India. It is found that the proposed algorithm is capable of estimating the pulled to sit score with sensitivity (80% 92% and specificity (89% 96%