BatchBMD as an Efficient and Accurate Dual-Energy X-ray Absorptiometry Report Generator

Dual-energy X-ray absorptiometry is the gold standard for evaluating Bone Mineral Density (BMD); however, a typical BMD report is generated in a time-inefficient manner and is prone to error. We developed a rule-based automated reporting system, BatchBMD, that accelerates DXA reporting while improving its accuracy over current systems. BatchBMD generates a structured report, customized to the specific clinical purpose. To compare BatchBMD to a Web-based Reporting (WBR) system for efficiency and accuracy, 500 examinations were randomly chosen from those performed at the Taipei Municipal Wanfang Hospital from January to March 2021. The final assessment included all 2326 examinations conducted from September 2020 to March 2021. The average reporting times were 6.7 and 10.8 minutes for BatchBMD and the WBR system, respectively, while accuracy was 99.4% and 98.2%, respectively. Most of the errors made by BatchBMD were digit errors in the appendicular skeletal muscle index. After correcting this, 100% accuracy across all 2326 examinations was validated. This automated and accurate BMD reporting system significantly reduces report production workload for radiologists and technicians while increasing productivity and quality. Additionally, the portable software, which employs a simple framework, can reduce deployment costs in clinical practice.

Specular Reflections Detection and Removal Based on Deep Neural Network for Endoscope Images

Specular reflections have always been undesirable when processing endoscope vision for clinical purpose. Scene afflicted with strong specular reflection could result in visual confusion for the operation of surgical robot. In this paper, we propose a novel model based on deep learning framework, known as Surgical Fix Deep Neural Network (SFDNN). This model can effectively detect and fix the reflection points in different surgical videos hence opening up a whole new approach in handling undesirable specular reflections.

Specular Reflections Detection and Removal Based on Deep Neural Network for Endoscope Images

Specular reflections have always been undesirable when processing endoscope vision for clinical purpose. Scene afflicted with strong specular reflection could result in visual confusion for the operation of surgical robot. In this paper, we propose a novel model based on deep learning framework, known as Surgical Fix Deep Neural Network (SFDNN). This model can effectively detect and fix the reflection points in different surgical videos hence opening up a whole new approach in handling undesirable specular reflections.

The Challenges of Developing Biosensors for Clinical Assessment: A Review

Emerging research in biosensors has attracted much attention worldwide, particularly in response to the recent pandemic outbreak of coronavirus disease 2019 (COVID-19). Nevertheless, initiating research in biosensing applied to the diagnosis of diseases is still challenging for researchers, be it in the preferences of biosensor platforms, selection of biomarkers, detection strategies, or other aspects (e.g., cutoff values) to fulfill the clinical purpose. There are two sides to the development of a diagnostic tool: the biosensor development side and the clinical side. From the development side, the research engineers seek the typical characteristics of a biosensor: sensitivity, selectivity, linearity, stability, and reproducibility. On the other side are the physicians that expect a diagnostic tool that provides fast acquisition of patient information to obtain an early diagnosis or an efficient patient stratification, which consequently allows for making assertive and efficient clinical decisions. The development of diagnostic devices always involves assay developer researchers working as pivots to bridge both sides whose role is to find detection strategies suitable to the clinical needs by understanding (1) the intended use of the technology and its basic principle and (2) the preferable type of test: qualitative or quantitative, sample matrix challenges, biomarker(s) threshold (cutoff value), and if the system requires a mono- or multiplex assay format. This review highlights the challenges for the development of biosensors for clinical assessment and its broad application in multidisciplinary fields. This review paper highlights the following biosensor technologies: magnetoresistive (MR)-based, transistor-based, quartz crystal microbalance (QCM), and optical-based biosensors. Its working mechanisms are discussed with their pros and cons. The article also gives an overview of the most critical parameters that are optimized by developing a diagnostic tool.

Distribution of multi-unit pitch responses recorded intracranially from human auditory cortex

The perception of pitch requires the abstraction of stimulus properties related to the spectrotemporal structure of sound. Previous studies utilizing both animal electrophysiology and human imaging have indicated the presence of a center for pitch representation in the auditory cortex. Recent data from our own group - examining local field potentials (LFPs) in humans - indicate more widely distributed pitch-associated responses within the auditory cortex (Gander et al., 2019). To probe this with greater spatial resolution, we examined multi-unit activity related to three different auditory stimuli, in seven epilepsy patients who were implanted with high-impedance electrodes in auditory cortex for the clinical purpose of localizing seizures. The stimuli were regular-interval noise (RIN) with a pitch strength that is related to the temporal regularity, and pitch value determined by repetition rate, and harmonic complexes with missing fundamentals. We demonstrated increases in spiking activity in 69 of 104 (66%) responsive multi-unit activity in auditory cortex due to pitch-associated stimuli. Importantly, these responses were distributed across the entire extent of Heschl's gyrus (HG), in both primary and non-primary areas, rather than isolated to a specific region, and this finding was evident regardless of the stimulus presented. These findings are the first multi-unit pitch responses recorded from humans, and align with a recent study in macaques (Kikuchi et al., 2019) demonstrating that both local field potential and unit responses to pitch-inducing stimuli are distributed throughout auditory cortex.

The challenges of developing biosensors for clinical assessment: a review

Emerging research in biosensors has attracted much attention worldwide, particularly in re-sponse to the recent pandemic outbreak of coronavirus disease 2019 (COVID-19). Neverthe-less, initiating research in biosensing applied to the diagnostic of diseases is still challenging for researchers, either from the preferences of biosensor platforms, selection of biomarkers, detection strategies, and other aspects (e.g., cutoff values) to fulfill the clinical purpose. There are two sides to the development of a diagnostic tool: the biosensor development side and the clinical side. From the development side, the research engineers seek the typical characteristics of a biosensor: sensitivity, selectivity, linearity, stability, and reproducibility. On the other side are the physicians that expect a diagnostic tool that provides fast acquisition of patient in-formation to obtain an early diagnostic or an efficient patient stratification, which conse-quently allows making assertive and efficient clinical decisions. The development of diagnostic devices always involves assay developer researchers, working as pivots to bridge both sides, which role is to find detection strategies suitable to the clinical needs. First, by understanding the intended use of the technology and its basic principle; second, the preferable type of test: qualitative or quantitative, sample matrix challenges, biomarker(s) threshold (cutoff value), and if the system requires a mono or multiplex assay format. This review highlights the challenges for the development of biosensors for clinical assessment and its broad application in multidisciplinary fields. This review paper highlights the following biosensor technologies: magnetoresistive (MR)-based, transistor-based, quartz crystal microbalance (QCM), and op-tical-based biosensors. Its working mechanisms are discussed with their pros and cons. The article also gives an overview of the most critical parameters that are optimized by developing a diagnostic tool.

752 Routine In-Hospital Radiographs Following Anterior Cervical Discectomy and Fusion Surgery: Neither Necessary nor Cost-Effective?

Aim To evaluate whether a post-operative radiograph of the cervical spine before discharge is either clinically justified or cost-effective in patients who have undergone an ACDF, despite having satisfactory intra-operative imaging. Method A retrospective review of 101 ACDF patients from a single neurosurgical centre. 78 had an in-hospital post-operative radiograph, 23 did not. In 95 of these, it was documented that there was ‘satisfactory intra-operative imaging’. All patients had intra-operative imaging of completed instrumentation. Any post-operative complications were noted, and the length of hospital stay (LOS) recorded. Study parameters also included: levels operated on, whether or not a plate was used with a cage, additional costings to the hospital. Results There was one post-operative complication relating to the metalwork in the 101 patients. However, the decision to perform this x-ray was based purely on the deteriorating post-operative clinical picture. In the cohort that had POXR’s, the average length of stay was 66.7 hours. Without POXR, it was 21 hours. The additional cost to the trust of performing the in-hospital radiographs was calculated to be £71,523 per year. Conclusions In patients who undergo ACDF surgery with an uneventful post-operative course and satisfactory intra-operative imaging, in-hospital post-operative radiographs serve no clinical purpose and delay discharge. This gives additional cost to the trust, unnecessary radiation exposure and occupies potential bedspace.

Accuracy of Three-Dimensional (3D) Printed Dental Digital Models Generated with Three Types of Resin Polymers by Extra-Oral Optical Scanning

Digital impression devices are used alternatively to conventional impression techniques and materials. The aim of this study was to evaluate the precision of extraoral digitalization of three types of photosensitive resin polymers used for 3D printing with the aid of a digital extraoral optical scanner. The alignment of the scans was performed by a standard best-fit alignment. Trueness and precision were used to evaluate the models. The trueness was evaluated by using bias as a measure and the standard deviation was used to evaluate the precision. After assessing the normality of the distributions, an independent Kruskal–Wallis test was used to compare the trueness and precision across the material groups. The Mann–Whitney test was used as a post-hoc test for significant differences. The result of the analysis showed significant differences (U = 66, z = −2.337, p = 0.019) in trueness of mesiodistal distances. Upon visual inspection of the models, defects were noticed on two out of nine of the models printed with a photosensitive polymer. The defects were presented as cavities caused by air bubbles and were also reflected in the scans. Mean precision did not vary too much between these three photosensitive polymer resins, therefore, the selection of 3D printing materials should be based on the trueness and the required precision of the clinical purpose of the model.

Automated white corpuscles nucleus segmentation using deep neural network from microscopic blood smear

The white corpuscles nucleus segmentation from microscopic blood images is major steps to diagnose blood-related diseases. The perfect and speedy segmentation system assists the hematologists to identify the diseases and take appropriate decision for better treatment. Therefore, fully automated white corpuscles nucleus segmentation model using deep convolution neural network, is proposed in the present study. The proposed model uses the combination of ‘binary_cross_entropy’ and ‘adam’ for maintaining learning rate in each network weight. To validate the potential and capability of the above proposed solution, ALL-IDB2 dataset is used. The complete set of images is partitioned into training and testing set and tedious experimentations have been performed. The best performing model is selected and the obtained training and testing accuracy of best performing model is reported as 98.69 % and 99.02 %, respectively. The staging analysis of proposed model is evaluated using sensitivity, specificity, Jaccard index, dice coefficient, accuracy and structure similarity index. The capability of proposed model is compared with performance of the region-based contour and fuzzy-based level-set method for same set of images and concluded that proposed model method is more accurate and effective for clinical purpose.

To study the selective potentiality and anti-tumor activity of CAP in oral cancer treatment

Oral Cancer is one of the leading causes of death in India, specifically in its Northeastern region. India holds about one third cases of the total oral cancer in the world. According to a report (2018), India holds 1,19,992 new cases of oral cancer and 72,616 deaths. Current approaches in cancer treatment mainly include surgery, chemotherapy and radiation therapy. These treatments often give a morbid outcome and reduce survival rate. This is mainly due to their non-selective nature to kill the tumor cells along with the normal cells. Recently the potential of Cold Atmospheric Plasma (CAP) in cancer treatment is gaining great interest among scientists and researchers. This is mainly due to the enhanced selective nature of the CAP in targeting and killing the tumor cells without affecting the surrounding tissue and causing minimal side effects. Reactive Oxygen and Nitrogen species (RONS) are the main contributors of the efficacy of CAP in killing cancer cells. Although many attempts have been made to understand the underlying mechanism of CAP in cancer treatment, there is a lack of detailed studies with respect to different treatment conditions. The use of CAP can be a potential innovative therapy in the upcoming days and more clinical trials should be encouraged to prove its efficacy and relevance in clinical purpose.