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Surgery Surgery

Augmented reality and artificial intelligence-assisted surgical navigation: Technique and cadaveric feasibility study.

In Journal of craniovertebral junction & spine

Purpose : Augmented reality-based image overlay of virtual bony spine anatomy can be projected onto real spinal anatomy using computer tomography-generated DICOM images acquired intraoperatively. The aim of the study was to develop a technique and assess the accuracy and feasibility of lumbar vertebrae pedicle instrumentation using augmented reality-assisted surgical navigation.

Subjects and Methods : An augmented reality and artificial intelligence (ARAI)-assisted surgical navigation system was developed. The system consists of a display system which hovers over the surgical field and projects three-dimensional (3D) medical images corresponding with the patient's anatomy. The system was registered to the cadaveric spine using an optical tracker and arrays with reflective markers. The virtual image overlay from the ARAI system was compared to 3D generated images from intraoperative scans and used to percutaneously navigate a probe to the cortex at the corresponding pedicle starting point. Intraoperative scan was used to confirm the probe position. Virtual probe placement was compared to the actual probe position in the bone to determine the accuracy of the navigation system.

Results : Four cadaveric thoracolumbar spines were used. The navigated probes were correctly placed in all attempted levels (n = 24 levels), defined as Zdichavsky type 1a, Ravi type I, and Gertzbein type 0. The virtual overlay image corresponded to the 3D generated image in all the tested levels.

Conclusions : The ARAI surgical navigation system correctly and accurately identified the starting points at all the attempted levels. The virtual anatomy image overlay precisely corresponded to the actual anatomy in all the tested scenarios. This technology may lead more uniform outcomes between surgeons and decrease minimally invasive spine surgery learning curves.

Siemionow Kris B, Katchko Karina M, Lewicki Paul, Luciano Cristian J

Augmented reality, minimally invasive spine surgery, pedicle screw accuracy, surgical navigation

Surgery Surgery

Autonomous image segmentation and identification of anatomical landmarks from lumbar spine intraoperative computed tomography scans using machine learning: A validation study.

In Journal of craniovertebral junction & spine

Purpose : Machine-learning algorithms are a subset of artificial intelligence that have proven to enhance analytics in medicine across various platforms. Spine surgery has the potential to benefit from improved hardware placement utilizing algorithms that autonomously and accurately measure pedicle and vertebral body anatomy. The purpose of this study was to assess the accuracy of an autonomous convolutional neural network (CNN) in measuring vertebral body anatomy utilizing clinical lumbar computed tomography (CT) scans and automatically segment vertebral body anatomy.

Methods : The CNN was trained utilizing 8000 manually segmented CT slices from 15 cadaveric specimens and 30 adult diagnostic scans. Validation was performed with twenty randomly selected patient datasets. Anatomic landmarks that were segmented included the pedicle, vertebral body, spinous process, transverse process, facet joint, and lamina. Morphometric measurement of the vertebral body was compared between manual measurements and automatic measurements.

Results : Automatic segmentation was found to have a mean accuracy ranging from 96.38% to 98.96%. Coaxial distance from the lamina to the anterior cortex was 99.10% with pedicle angulation error of 3.47%.

Conclusion : The CNN algorithm tested in this study provides an accurate means to automatically identify the vertebral body anatomy and provide measurements for implants and placement trajectories.

Siemionow Krzyzstof, Luciano Cristian, Forsthoefel Craig, Aydogmus Suavi

Artificial intelligence, navigation, spine surgery

General General

Classification of Coronavirus (COVID-19) from X-ray and CT images using shrunken features.

In International journal of imaging systems and technology

Necessary screenings must be performed to control the spread of the COVID-19 in daily life and to make a preliminary diagnosis of suspicious cases. The long duration of pathological laboratory tests and the suspicious test results led the researchers to focus on different fields. Fast and accurate diagnoses are essential for effective interventions for COVID-19. The information obtained by using X-ray and Computed Tomography (CT) images is vital in making clinical diagnoses. Therefore it is aimed to develop a machine learning method for the detection of viral epidemics by analyzing X-ray and CT images. In this study, images belonging to six situations, including coronavirus images, are classified using a two-stage data enhancement approach. Since the number of images in the dataset is deficient and unbalanced, a shallow image augmentation approach was used in the first phase. It is more convenient to analyze these images with hand-crafted feature extraction methods because the dataset newly created is still insufficient to train a deep architecture. Therefore, the Synthetic minority over-sampling technique algorithm is the second data enhancement step of this study. Finally, the feature vector is reduced in size by using a stacked auto-encoder and principal component analysis methods to remove interconnected features in the feature vector. According to the obtained results, it is seen that the proposed method has leveraging performance, especially to make the diagnosis of COVID-19 in a short time and effectively. Also, it is thought to be a source of inspiration for future studies for deficient and unbalanced datasets.

Öztürk Şaban, Özkaya Umut, Barstuğan Mücahid

2020-Aug-18

COVID‐19, classification, coronavirus, feature extraction, hand‐crafted features, sAE

Surgery Surgery

Enhancing India's Health Care during COVID Era: Role of Artificial Intelligence and Algorithms.

In Indian journal of otolaryngology and head and neck surgery : official publication of the Association of Otolaryngologists of India

Computerization of health care is the only model to sustain safe health care in this COVID era particularly in overpopulated nations with limited health care providers/systems like India. Accordingly incorporation of computer-based algorithms and artificial intelligence seems very robust and practical models to assist the physician. The advantages of Computerized algorithms to facilitate better screening, diagnosis or follow-up and use of Artificial Intelligence (AI) to aid in medical diagnosis are discussed.

Katyayan Angira, Katyayan Adri, Mishra Anupam

2020-Sep-01

Artificial intelligence, COVID-19, Computer algorithms

Pathology Pathology

Plant science decadal vision 2020-2030: Reimagining the potential of plants for a healthy and sustainable future.

In Plant direct

Plants, and the biological systems around them, are key to the future health of the planet and its inhabitants. The Plant Science Decadal Vision 2020-2030 frames our ability to perform vital and far-reaching research in plant systems sciences, essential to how we value participants and apply emerging technologies. We outline a comprehensive vision for addressing some of our most pressing global problems through discovery, practical applications, and education. The Decadal Vision was developed by the participants at the Plant Summit 2019, a community event organized by the Plant Science Research Network. The Decadal Vision describes a holistic vision for the next decade of plant science that blends recommendations for research, people, and technology. Going beyond discoveries and applications, we, the plant science community, must implement bold, innovative changes to research cultures and training paradigms in this era of automation, virtualization, and the looming shadow of climate change. Our vision and hopes for the next decade are encapsulated in the phrase reimagining the potential of plants for a healthy and sustainable future. The Decadal Vision recognizes the vital intersection of human and scientific elements and demands an integrated implementation of strategies for research (Goals 1-4), people (Goals 5 and 6), and technology (Goals 7 and 8). This report is intended to help inspire and guide the research community, scientific societies, federal funding agencies, private philanthropies, corporations, educators, entrepreneurs, and early career researchers over the next 10 years. The research encompass experimental and computational approaches to understanding and predicting ecosystem behavior; novel production systems for food, feed, and fiber with greater crop diversity, efficiency, productivity, and resilience that improve ecosystem health; approaches to realize the potential for advances in nutrition, discovery and engineering of plant-based medicines, and "green infrastructure." Launching the Transparent Plant will use experimental and computational approaches to break down the phytobiome into a "parts store" that supports tinkering and supports query, prediction, and rapid-response problem solving. Equity, diversity, and inclusion are indispensable cornerstones of realizing our vision. We make recommendations around funding and systems that support customized professional development. Plant systems are frequently taken for granted therefore we make recommendations to improve plant awareness and community science programs to increase understanding of scientific research. We prioritize emerging technologies, focusing on non-invasive imaging, sensors, and plug-and-play portable lab technologies, coupled with enabling computational advances. Plant systems science will benefit from data management and future advances in automation, machine learning, natural language processing, and artificial intelligence-assisted data integration, pattern identification, and decision making. Implementation of this vision will transform plant systems science and ripple outwards through society and across the globe. Beyond deepening our biological understanding, we envision entirely new applications. We further anticipate a wave of diversification of plant systems practitioners while stimulating community engagement, underpinning increasing entrepreneurship. This surge of engagement and knowledge will help satisfy and stoke people's natural curiosity about the future, and their desire to prepare for it, as they seek fuller information about food, health, climate and ecological systems.

Henkhaus Natalie, Bartlett Madelaine, Gang David, Grumet Rebecca, Jordon-Thaden Ingrid, Lorence Argelia, Lyons Eric, Miller Samantha, Murray Seth, Nelson Andrew, Specht Chelsea, Tyler Brett, Wentworth Thomas, Ackerly David, Baltensperger David, Benfey Philip, Birchler James, Chellamma Sreekala, Crowder Roslyn, Donoghue Michael, Dundore-Arias Jose Pablo, Fletcher Jacqueline, Fraser Valerie, Gillespie Kelly, Guralnick Lonnie, Haswell Elizabeth, Hunter Mitchell, Kaeppler Shawn, Kepinski Stefan, Li Fay-Wei, Mackenzie Sally, McDade Lucinda, Min Ya, Nemhauser Jennifer, Pearson Brian, Petracek Peter, Rogers Katie, Sakai Ann, Sickler Delanie, Taylor Crispin, Wayne Laura, Wendroth Ole, Zapata Felipe, Stern David

2020-Aug

research areas, research methods, research organisms

Pathology Pathology

Attempt to Predict A/T/N-Based Alzheimer's Disease Cerebrospinal Fluid Biomarkers Using a Peripheral Blood DNA Methylation Clock.

In Journal of Alzheimer's disease reports

Background : Although aging is the strongest risk factor for the development of Alzheimer's disease (AD), it remains uncertain if the blood DNA methylation clock, which reflects the effect of biological aging on DNA methylation (DNAme) status of blood cells, may be used as a surrogate biomarker for AD pathology in the central nervous system (CNS).

Objective : We aimed to develop a practical model to predict for A/T/N-based AD biomarkers as the prediction targets using the aging acceleration of blood cells.

Methods : We obtained data of North American ADNI study participants (n = 317) whose blood DNA methylation microarray (Illumina HumanMethylation EPIC Beadchips) and cerebrospinal fluid (CSF) AD biomarkers (Aβ, t-tau, and p-tau) were recorded simultaneously. Methylation clock was calculated to conduct machine learning, in order to predict binary statuses (+ or -) for A (corresponding to the lowered CSF Aβ), T (the elevated CSF p-tau), or N (the elevated CSF t-tau). The predictive performance of the models was evaluated by area under curve (AUC) in the test subset within ADNI.

Results : Among the 317 included samples, 194 (61.2%) were A+, 247 (77.9%) were T+, and 104 (32.8%) were N+. The degree of blood aging acceleration showed weak positive correlation with the CSF Aβ levels, even after adjustment with APOE genotype and other covariates. However, the contribution of aging acceleration to improve the predictive performance of models was not significant for any of A+, T+, or N+.

Conclusion : Our exploratory attempts could not demonstrate the substantial utility of the peripheral blood cells' methylation clock as a predictor for A/T/N-based CSF biomarkers of AD, and further additional work should be conducted to determine whether the blood DNAme signatures including methylation clock have substantial utility in detecting underlying amyloid, tau or neurodegeneration pathology of AD.

Sato Kenichiro, Mano Tatsuo, Suzuki Kazushi, Toda Tatsushi, Iwatsubo Takeshi, Iwata Atsushi

2020-Jul-23

A/T/N, Alzheimer’s disease, DNA methylation, blood biomarker, epigenetic clock