Detection of Twin Pregnancies using Fetal Phonocardiogram.

Fetal phonocardiogram (fPCG), or the electronic recording of fetal heart sounds, is a safe and easily available signal that can be used to monitor fetal wellbeing. In the proposed work an attempt is made to identify twin pregnancies using fPCG data recorded from the fetus with 1/3rd power in octave band filtered output as features to train K-Nearest Neighbor (KNN) and support vector machine (SVM) classifiers. The SVM classifier with the quadratic kernel is able to identify singletons and twins with a positive predictive value of 100% and 79.1% respectively. The KNN classifier with k=10 neighbors is able to identify singletons and twins with a positive predictive value of 100% and 81.8% respectively.Clinical Relevance: Identifying twin pregnancies from singleton is an essential clinical protocol followed during late pregnancy as there may be complications like twin-twin transfusion syndrome, selective fetal growth restriction, and preterm labor in twin pregnancy [1], [2]. Ultrasound imaging is the most commonly used technique for twin pregnancy detection, though it is often not affordable or available in rural or low-income populations. Utilization of fPCG in such circumstances has immense clinical potential.

A Bin-Based Indexing for Scalable Range Join on Genomic Data.

Range-join is an operation for finding overlaps in interval-form genomic data. Range-join is widely used in various genome analysis processes such as annotation, filtering and comparison of variants in whole-genome and exome analysis pipelines. The quadratic complexity of current algorithms with sheer data volume has surged the design challenges. Existing tools have limitations on algorithm efficiency, parallelism, scalability and memory consumption. This paper proposes BIndex, a novel bin-based indexing algorithm and its distributed implementation to attain high throughput range-join processing. BIndex features near-constant search complexity while the inherently parallel data structure facilitates exploitation of parallel computing architectures. Balanced partitioning of dataset further enables scalability on distributed frameworks. The implementation on Message Passing Interface shows upto 933.5x speedup in comparison to state-of-the-art tools. Parallel nature of BIndex further enables GPU-based acceleration with 3.72x speedup than CPU implementations. The add-in modules for Apache Spark provides upto 4.65x speedup than the previously best available tool. BIndex supports wide variety of input and output formats prevalent in bioinformatics community and the algorithm is easily extendable to streaming data in recent Big Data solutions. Furthermore, the index data structure is memory-efficient and consumes upto two orders-of-magnitude lesser RAM, while having no adverse effect on speedup.

SegPC-2021: A challenge & dataset on segmentation of Multiple Myeloma plasma cells from microscopic images.

Multiple Myeloma (MM) is an emerging ailment of global concern. Its diagnosis at the early stages is critical for recovery. Therefore, efforts are underway to produce digital pathology tools with human-level intelligence that are efficient, scalable, accessible, and cost-effective. Following the trend, a medical imaging challenge on "Segmentation of Multiple Myeloma Plasma Cells in Microscopic Images (SegPC-2021)" was organized at the IEEE International Symposium on Biomedical Imaging (ISBI), 2021, France. The challenge addressed the problem of cell segmentation in microscopic images captured from the slides prepared from the bone marrow aspirate of patients diagnosed with Multiple Myeloma. The challenge released a total of 775 images with 690 and 85 images of sizes 2040×1536 and 1920×2560 pixels, respectively, captured from two different (microscope and camera) setups. The participants had to segment the plasma cells with a separate label on each cell's nucleus and cytoplasm. This problem comprises many challenges, including a reduced color contrast between the cytoplasm and the background, and the clustering of cells with a feeble boundary separation of individual cells. To our knowledge, the SegPC-2021 challenge dataset is the largest publicly available annotated data on plasma cell segmentation in MM so far. The challenge targets a semi-automated tool to ensure the supervision of medical experts. It was conducted for a span of five months, from November 2020 to April 2021. Initially, the data was shared with 696 people from 52 teams, of which 41 teams submitted the results of their models on the evaluation portal in the validation phase. Similarly, 20 teams qualified for the last round, of which 16 teams submitted the results in the final test phase. All the top-5 teams employed DL-based approaches, and the best mIoU obtained on the final test set of 277 microscopic images was 0.9389. All these five models have been analyzed and discussed in detail. This challenge task is a step towards the target of creating an automated MM diagnostic tool.

Pocket CLARITY enables distortion-mitigated cardiac microstructural tissue characterization of large-scale specimens.

Molecular phenotyping by imaging of intact tissues has been used to reveal 3D molecular and structural coherence in tissue samples using tissue clearing techniques. However, clearing and imaging of cardiac tissue remains challenging for large-scale (>100 mm3) specimens due to sample distortion. Thus, directly assessing tissue microstructural geometric properties confounded by distortion such as cardiac helicity has been limited. To combat sample distortion, we developed a passive CLARITY technique (Pocket CLARITY) that utilizes a permeable cotton mesh pocket to encapsulate the sample to clear large-scale cardiac swine samples with minimal tissue deformation and protein loss. Combined with light sheet auto-fluorescent and scattering microscopy, Pocket CLARITY enabled the characterization of myocardial microstructural helicity of cardiac tissue from control, heart failure, and myocardial infarction in swine. Pocket CLARITY revealed with high fidelity that transmural microstructural helicity of the heart is significantly depressed in cardiovascular disease (CVD), thereby revealing new insights at the tissue level associated with impaired cardiac function.

Synchrosqueezed Transform based Click Event Segmentation in Phonocardiogram of Mitral Valve Prolapse.

Phonocardiogram (PCG) signal of the mitral valve prolapse (MVP) patients is characterized by transient audio events which include a systolic click (SC) followed by a murmur of varying intensity. Physicians detect these auscultation clues in regular auscultation before ordering expensive echocardio-graphy test. But auscultation is often error prone and even physicians with considerable experience might end up missing these clues. Therefore developing machine learning techniques to help clinicians is the need of the hour. A segmentation technique using Fourier synchrosqueezed transform (FSST) features with a long short term memory (LSTM) network is proposed in this study. An accuracy of 99.8% on MVP dataset demonstrates the potential of the proposed method in clinical diagnosis.

Digital biomarkers of spine and musculoskeletal disease from accelerometers: Defining phenotypes of free-living physical activity in knee osteoarthritis and lumbar spinal stenosis.

BACKGROUND CONTEXT: Lumbar spinal stenosis (LSS) and knee osteoarthritis (OA) are 2 of the leading causes of disability worldwide. In order to provide disease-specific prescriptions for physical activity, there is a clear need to better understand physical activity in daily life (performance) in these populations. PURPOSE: To discover performance phenotypes for LSS and OA by applying novel analytical methods to accelerometry data. Specific objectives include the following: (1) to identify characteristic features (phenotypes) of free-living physical activity unique to individuals with LSS and OA, and (2) to determine which features can best differentiate between these conditions. STUDY DESIGN AND SETTING: Leveraging data from 3 existing cross-sectional cohorts, accelerometry signal feature characterization and selection were performed in a computational laboratory. PATIENT SAMPLE: Data from a total of 4,028 individuals were analyzed from the following 3 datasets: LSS Accelerometry Database (n=75); OA Initiative (n=1950); and the 2003 to 2004 National Health and Nutrition Examination Survey (pain-free controls, n=2003). METHODS: In order to characterize the accelerometry signals, data were examined using (1) standard intervals for counts/minute from Freedson et al. and (2) the physical performance intervals for mobility-limited pain populations. From this, 42 novel accelerometry features were defined and evaluated for significance in discriminating between the groups (LSS, OA, and controls) in order to then determine which sparse set of features best differentiates between the groups. These sparse sets of features defined the performance phenotypes. OUTCOME MEASURES: Accelerometry features and their ability to differentiate between individuals with LSS, OA, and controls. RESULTS: Given age and gender, classification rates were at least 80% accurate (pairwise) between diseased and pain-free populations (LSS vs. controls and OA vs. controls). The most important features to distinguish between disease groups corresponded to measures in the light and sedentary activity intervals. The more subtle classification between diseased populations (LSS vs. OA) was 72% accurate, with light and moderate activity providing the prominent distinguishing features. CONCLUSIONS: We describe the discovery of performance phenotypes of LSS and OA from accelerometry data, revealed through a novel set of features that characterize daily patterns of movement in people with LSS and OA. These performance phenotypes provide a new method for analyzing free-living physical activity (performance) in LSS and OA, and provide the groundwork for more personalized approaches to measuring and improving function.

Objective measurement of function following lumbar spinal stenosis decompression reveals improved functional capacity with stagnant real-life physical activity.

BACKGROUND CONTEXT: Lumbar spinal stenosis (LSS) is a prevalent and costly condition associated with significant dysfunction. Alleviation of pain and improvement of function are the primary goals of surgical intervention. Although prior studies have measured subjective improvements in function after surgery, few have examined objective markers of functional improvement. PURPOSE: We aimed to objectively measure and quantify changes in physical capacity and physical performance following surgical decompression of LSS. STUDY DESIGN/SETTING: Prospective cohort study. PATIENT SAMPLE: Thirty-eight patients with LSS determined by the treating surgeon's clinical and imaging evaluation, and who were scheduled for surgical treatment, were consecutively recruited at two academic medical facilities, with 28 providing valid data for analysis at baseline and 6 months after surgery. OUTCOME MEASURES: Before surgery and at 6 months after surgery, participants provided 7 days of real-life physical activity (performance) using ActiGraph accelerometers; completed two objective functional capacity measures, the Short Physical Performance Battery and Self-Paced Walking Test; and completed three subjective functional outcome questionnaires, Oswestry Disability Index, Spinal Stenosis Symptom Questionnaire, and Short-Form 36. METHODS: Physical activity, as measured by continuous activity monitoring, was analyzed as previously described according to the 2008 American Physical Activity Guidelines. Paired t tests were performed to assess for postsurgical changes in all questionnaire outcomes and all objective functional capacity measures. Chi-square analysis was used to categorically assess whether patients were more likely to meet these physical activity recommendations after surgery. RESULTS: Participants were 70.1 years old (±8.9) with 17 females (60.7%) and an average body mass index of 28.4 (±6.2). All subjective measures (Oswestry Disability Index, Spinal Stenosis Symptom Questionnaire, and Short-Form 36) improved significantly at 6 months after surgery, as did objective functional measures of capacity including balance, gait speed, and ambulation distance (Short Physical Performance Battery, Self-Paced Walking Test). However, objectively measured performance (real-life physical activity) did not change following surgery. Although fewer participants qualified as inactive (54% vs. 71%), and more (11% vs. 4%) met the physical activity guideline recommendations at the 6-month follow-up, these differences were not statistically significant (p=.22) CONCLUSIONS: This is the first study, of which we are aware, to objectively evaluate changes in postsurgical performance (real-life physical activity) in people with LSS. We found that at 6 months after surgery for LSS, participants demonstrated significant improvements in self-reported function and objectively measured physical capacity, but not physical performance as measured by continuous activity monitoring. This lack of improvement in performance, despite improvements in self-reported function and objective capacity, suggests a role for postoperative rehabilitation focused specifically on increasing performance after surgery in the LSS population.

Objective measurement of free-living physical activity (performance) in lumbar spinal stenosis: are physical activity guidelines being met?

BACKGROUND: Research suggests that people with lumbar spinal stenosis (LSS) would benefit from increased physical activity. Yet, to date, we do not have disease-specific activity guidelines for LSS, and the nature of free-living physical activity (performance) in LSS remains unknown. LSS care providers could endorse the 2008 United States Physical Activity Guidelines; however, we do not know if this is realistic. The goal of the present study was to determine the proportion of individuals with LSS meeting the 2008 Guidelines. A secondary goal was to better understand the nature of physical performance in this population. STUDY DESIGN: Retrospective study. PATIENT SAMPLE: People from the Lumbar Spinal Stenosis Accelerometry Database, all of whom have both radiographic and clinical LSS and are seeking various treatments for their symptoms. OUTCOME MEASURES: Seven-day accelerometry (functional outcome) and demographics (self-reported). METHODS: For the present study, we analyzed only baseline data that were obtained before any new treatments. Patients with at least 4 valid days of baseline accelerometry data were included. We determined the proportion of individuals with LSS meeting the 2008 US Physical Activity Guidelines of at least 150 minutes of moderate-vigorous (MV) physical activity per week in bouts of 10 minutes or more. We also used the novel Physical Performance analysis designed by our group to determine time spent in varying intensities of activity. There are no conflicts of interest to disclose. RESULTS: We analyzed data from 75 individuals with a mean age of 68 (SD 9), 37% of whom were male. Three people (4%) were considered Meeting Guidelines (at least 150 MV minutes/week), and 56 (75%) were considered Inactive with not even 1 MV minute/week. With the 10-minute bout requirement removed, 10 of 75 (13%) achieved the 150-minute threshold. The average time spent in sedentary activity was 82%, and of time spent in nonsedentary activity, 99.6% was in the light activity range. CONCLUSIONS: In conclusion, the present study confirms that people with symptomatic LSS, neurogenic claudication, walking limitations, and LSS-related disability are extremely sedentary and are not meeting guidelines for physical activity. There is an urgent need for interventions aimed at reducing sedentary behavior and increasing the overall level of physical activity in LSS, not only to improve function but also to prevent diseases of inactivity. The present study suggests that reducing sedentary time, increasing time spent in light intensity activity, and increasing time spent in higher intensities of light activity may be appropriate as initial goals for exercise interventions in people with symptomatic LSS and neurogenic claudication, transitioning to moderate activity when appropriate. Results of the present study also demonstrate the importance of employing disease-specific measures for assessment of performance in LSS, and highlight the potential value of these methods for developing targeted and realistic goals for physical activity. Physical activity goals could be personalized using objective assessment of performance with accelerometry. The present study is one step toward a personalized medicine approach for people with LSS, focusing on increasing physical function.