Analysis of Grunting Sound in Infants for Predicting the Severity of Respiratory Distress Syndrome.

OBJECTIVE: Vocalizations from infants, particularly sounds associated with respiratory distress, are fundamental for observational scoring of respiratory tract issues. Listening to these infant sounds is a prevalent technique for decision-making in newborn intensive care units. Expiratory grunting, indicative of the severity and presence of potential conditions, is valuable, however, this evaluative method is subjective and prone to error. This study investigates the potential of computer-aided analysis to offer an objective scale for assessing the severity of respiratory tract problems, utilizing digital recordings of grunting sounds. METHODS: The original data set is formed with a total of 189 grunting sound segments collected from 38 infants. Multiple evaluation approaches were performed to reveal the relation between spectral characteristics of the recordings and the severity or existence of respiratory distress. RESULTS: Three spectral features were evaluated as prominently related to hospital stay duration and respiratory distress. The harmonic ratio of the recordings was graded as the most-related spectral feature that would characterize the severity. CONCLUSIONS: The potential of an innovative and objective grading approach is first investigated for replacing the human ear with a computer-aided evaluation system. The results are promising and the detected relation between expert ear-based scoring and harmonic ratio suggests that the spectral character of the grunting sounds would reflect the nature of respiratory conditions. Moreover, this study underlines those spectral features of digital grunting recordings that would be functional for automated prediction and decision-making.

Morphometric analysis of the effects of high molecular weight sodium hyaluronate and amino acids mixture on face rejuvenation.

BACKGROUND: Skin aging can be observed at various levels in the epidermis, dermis, and dermo-epidermal junction. Reducing the cosmetic effects of skin aging in the facial region is a widespread demand due to common aesthetic concerns. Consequently, many injectable products on the market promise antiaging effects and cosmetic improvements. We aimed to evaluate the cosmetic efficacy of a high molecular weight sodium hyaluronate and amino acids mixture for the facial region using morphometric analysis. METHODS: This study evaluates the morphometric effectiveness of an injectable mixture (high molecular weight sodium hyaluronate, glycine, L-Proline, L-leucine, L-lysine HCL, L-valine, and L-alanine collagen active ingredient) on the mid-face and jawline in women aged 30-55. We used morphological measurements and digital image data to assess changes and determine effectiveness. Various computational methods were applied simultaneously with statistical tests for validation. RESULTS: The hydration assessment showed a significant increase on both sides of the face. A noticeable decrease was observed in gonion angle, bitragion breadth, bigonion breadth, and marionette wrinkle scale. These results suggest combining mechanical and chemical stimulation from the injection and its components (hyaluronic and amino acids) effectively enhances skin quality. CONCLUSIONS: The study indicates that the mechanical stimulation of the injection improves skin quality. Combining hyaluronic and amino acids (collagen, elastin, and pro-synthetic) is a safe and effective alternative for antiaging treatments.

A Cluster Validity Index-Based Objective Criteria for Aesthetic Evaluation of Periorbital Treatment.

Purpose: Dark circles and pigmentation around the eyes are common reasons people see dermatologists. An effective assessment of the severity of infraorbital color and texture differences is critical for determining appropriate treatment. Evaluation of the visual severity of cases is mostly based on visual inspection. Treatment efficiency is often measured using patient questionnaires in many cases. The subjectivity of assessments may lead to a prolonged healing process, misdiagnosis, and excessive use of drugs or chemicals. Patients and Methods: In this study, a computer-aided objective evaluation approach was proposed for grading periorbital facial rejuvenation. This approach is based on the analysis of numerical features extracted from different facial regions in digital images. Healing was objectively graded by evaluating data clusters formed using the extracted features. Accordingly, an increase in the visual similarity between paired facial regions is accepted as an indicator of healing, which directly affects the form of data clusters. An intracluster validity index was measured to evaluate the clusters as dense and well separated. A total of 144 facial regions were extracted and examined, and the automatically calculated grades were compared with expert evaluations. Results: The cosmetic effects of the experimental drug were evaluated during the experiments, and expert grades were accepted as the ground truth. The results show that the proposed automated grading approach can evaluate rejuvenation with an accuracy of up to 0.91 accuracy in the upper orbital region. Conclusion: This study concluded that the proposed data-clustering-based approach is promising and can be functional without any special instruments.

Assessing the Rejuvenation Effectiveness of a Hyaluronic Acid and Amino Acid Mixture in the Periorbital Region.

Purpose: We performed an assessment of the rejuvenation effect of an amino acid and hyaluronic acid mixture in the periorbital area. Methods: A total of 23 of the 35 participants completed all application sessions and measurements. These 23 women were aged 30-55 years. A hyaluronic acid and amino acid mixture was injected into the participants' periorbital area. Three sessions of application with 15-day intervals were undertaken. Subjects' age, height, weight, smoking status, and sport participation were recorded. A photonumeric dark circle scale and Fitzpatrick's periorbital wrinkling classification were used for evaluation of dark circles and wrinkles in the periorbital area. Anatomical measurements (height of upper and lower eyelids) were done using ImageJ and a skin-analysis system (Observ 520). Results: The 23 women had a mean age of 42.46±9.33 years, mean height 164.46±4.96 cm, and mean weight 63.94±8.26 kg. Before the sessions, the mean heights of the upper eyelids were 1.24±013 cm (right) and 1.21±013 cm (left), while those of the lower eyelids were 0.98±014 cm (right) and 0.97±0.17 cm (left). One month after the third session, mean upper-eyelid heights were 1.30±0.09 cm (right) and 1.28±0.11 cm (left) and lower-eyelid ones 1.02±0.11 cm (right) and 1.02±0.13 cm (left). Dark-circle and wrinkle-scale scores showed significantly positive results between before the sessions and 1 month after the third session. Conclusion: A hyaluronic acid and amino acid mixture can be used to rejuvenation of the periorbital area in women aged 30-55 years.

Investigation of Relation Between Hypoxic-Ischemic Encephalopathy and Spectral Features of Infant Cry Audio.

Despite advances in medical technologies, Hypoxic-Ischemic Encephalopathy (HIE) continues to be a problem for neonatal intensive care units. Analysis of crying sounds may be a valuable tool for predicting neonatal disease. However, the characteristics of crying in newborns with HIE are still unclear. One of the factors limiting the ability to focus on that subject is the lack of commercially available infant cry database for research. Also, another reason that complicates the classification is the varying characteristics of infant cry. Accordingly, crying sounds were recorded from 35 infants and demographic characteristics of the study groups are presented as well as the numerical representation of spectral features. Experiments reveal that the existence of HIE causes distinctive variation in energy, energy entropy and spectral centroid features of the utterances; which leads us to conclude that the presented combination of spectral features would function well with any supervised or unsupervised machine learning algorithm.

A novel evolutionary method for spine detection in ultrasound samples of spina bifida cases.

BACKGROUND AND OBJECTIVES: Spina bifida is a fetal spine defect observed during pregnancy. The defect is caused by unfinished closure of the embryonic neural column. Common diagnosis of the defect is still based on manual examination which aims to detect any deformation on spinal axis. This study proposes a novel evolutionary method for locating spinal axis on sonograms of spina bifida pathology. METHODS: The method involves a meta-heuristic evolutionary approach, where the sonogram is automatically divided into columns and bone regions belonging to the spine are classified. Accordingly, a specific genetic algorithm is utilized which constructs a set of candidate spine axes. Fitness of the candidate axes is measured by a proposed problem-specific fitness function. A combination of conventional genetic operators and a novel energy minimization approach is applied to each population in order to explore the problem search space. RESULTS: Results show that presented algorithm is generally able to distinguish the spinal bones from others even in the presence of severe morphological defects. CONCLUSION: It is observed that the presented approach is promising and in most samples the spines identified by the proposed algorithm closely match those drawn by the experts. A computer assisted ultrasound diagnosis system specialized for spina bifida cases does not exist yet, but an algorithm to identify the spine, such as the one presented in this work, is the first natural step towards a diagnosis system. In the future, we intend to improve the algorithm by improving the segmentation stage and further optimizing the various stages of the genetic algorithm.

A Nature-Inspired Search Space Reduction Technique for Spine Identification on Ultrasound Samples of Spina Bifida Cases.

Spina bifida is a birth defect caused by incomplete closing around the spinal cord. Spina bifida is diagnosed in a number of different ways. One approach involves searching for a deformity in the spinal axis via ultrasound. Although easy to apply, this approach requires a highly trained clinician to locate the abnormality due to the noise and distortion present in prenatal ultrasound images. Accordingly, visual examination of ultrasound images may be error prone and subjective. A computerized support system that would automatically detect the location of the spinal deformity may be helpful to the clinician in the diagnostic process. Such a software system first and foremost would require an algorithm for the identification of the entire (healthy or unhealthy) spine in the ultrasound image. This paper introduces a novel flocking dynamics based approach for reducing the size of the search space in the spine identification problem. Proposed approach accepts bone-like blobs on the ultrasound images as bird flocks and combine them into bone groups by calculating the migration path of each flock. Presented results reveal that the method is able to locate correct bones to be grouped together and reduce search space (i.e. number of bones) up to 68%.

Data cluster analysis-based classification of overlapping nuclei in Pap smear samples.

BACKGROUND: The extraction of overlapping cell nuclei is a critical issue in automated diagnosis systems. Due to the similarities between overlapping and malignant nuclei, misclassification of the overlapped regions can affect the automated systems' final decision. In this paper, we present a method for detecting overlapping cell nuclei in Pap smear samples. METHOD: Judgement about the presence of overlapping nuclei is performed in three steps using an unsupervised clustering approach: candidate nuclei regions are located and refined with morphological operations; key features are extracted; and candidate nuclei regions are clustered into two groups, overlapping or non-overlapping, A new combination of features containing two local minima-based and three shape-dependent features are extracted for determination of the presence or absence of overlapping. F1 score, precision, and recall values are used to evaluate the method's classification performance. RESULTS: In order to make evaluation, we compared the segmentation results of the proposed system with empirical contours. Experimental results indicate that applied morphological operations can locate most of the nuclei and produces accurate boundaries. Independent features significance test indicates that our feature combination is significant for overlapping nuclei. Comparisons of the classification results of a fuzzy clustering algorithm and a non-fuzzy clustering algorithm show that the fuzzy approach would be a more convenient mechanism for classification of overlapping. CONCLUSION: The main contribution of this study is the development of a decision mechanism for identifying overlapping nuclei to further improve the extraction process with respect to the segmentation of interregional borders, nuclei area, and radius. Experimental results showed that our unsupervised approach with proposed feature combination yields acceptable performance for detection of overlapping nuclei.