Artificial Imaging Research Articles

Artificial Hyper Compound Eyes Enable Variable-Focus Imaging on both Curved and Flat Surfaces.

The artificial compound eye (ACE) with zoom imaging requires complex power sources. Meanwhile, its curved substrate makes it difficult for the ACE to realize the zoom imaging on flat surfaces. To realize a wide field of view and a zoom function on both curved and flat surfaces simultaneously, a novel ACE is proposed, which is a bionic design inspired by an ancient creature, trilobite. Compared with a dragonfly, photosensitive units of a trilobite's compound eye are composed of ommatidia with different focal lengths. By learning from this concept, an artificial hyper compound eye (AHCE) was fabricated. Its basic components are five microlenses with different curvatures, and they are capable of being treated as five ommatidia with different focal lengths. Five ommatidia form a photosensitive unit to realize a zoom function. AHCE is capable of variable-focus imaging on curved surfaces. With the information share function, we found that the AHCE not only images on curved surfaces but also has a zoom-imaging function on flat surfaces. The results confirm that the AHCE demonstrates an advanced imaging capability, a variable-focus imaging function on both curved and flat surfaces, which may open new opportunities in developing advanced micro-optical devices.

Guest Editorial Artificial Intelligence in Pre-DICOM

The papers in this special section focus on artificial intelligence pre-DICOM medical imaging. AI for medical imaging is applied in three domains: pre-DICOM, pre-processing and clinical applications. Clinical applications mainly cover topics such as disease detection, classification, segmentation, registration. Pre-processing components are mainly designed for facilitating applications using image transformation such as image normalization, noise reduction, bias correction in MR. AI in the pre-DICOM domain is expected to improve imaging workflow, image protocol selection, imaging quality, imaging scanning time before images are converted into DICOM format for radiologists to review. The trends of AI publications in medical imaging have been gradually extended from clinical applications to pre-processing and, to pre-DICOM. The papers in this special section seek to present and highlight the latest development on applying advanced deep learning techniques in pre-DICOM space. The papers highlight the latest development on applying advanced deep learning techniques in pre-DICOM space.

History of ARIES: a premier research institute in the area of observational sciences

The Aryabhatta Research Institute of Observational Sciences (ARIES), a premier autonomous research institute under the Department of Science and Technology, Government of India has a legacy of about seven decades with contributions made in the field of observational sciences namely atmospheric and astrophysics. The Survey of India used a location at ARIES, determined with an accuracy of better than 10 m on a world datum through institute’s participation in a global network of Earth artificial satellites imaging during late 1950’s. Taking advantage of its high-altitude location, ARIES, for the first time, provided valuable input for climate change studies by long term characterization of physical and chemical properties of aerosols and trace gases in the central Himalayan regions. In astrophysical sciences, the institute has contributed precise and sometime unique observations of the celestial bodies leading to a number of discoveries. With the installation of the 3.6-m Devasthal optical telescope in the year 2015, India became the only Asian country to join those few nations of the world who are hosting 4-m class optical telescopes. This telescope, having advantage of geographical location, is well-suited for multi-wavelength observations and for sub-arc-second resolution imaging of the celestial objects including follow-up of the GMRT, AstroSat and gravitational-wave sources.

A bibliometric analysis of neuroimaging biomarkers in Parkinson disease based on Web of Science.

Background:This study aimed to analyze and summarize the research hotspots and trends in neuroimaging biomarkers (NMBM) in Parkinson disease (PD) based on the Web of Science core collection database and provide new references for future studies.Methods:Literature regarding NMBM in PD from 1998 to 2022 was analyzed using the Web of Science core collection database. We utilized CiteSpace software (6.1R2) for bibliometric analyses of countries/institutions/authors, keywords, keyword bursts, references, and their clusters.Results:A total of 339 studies were identified with a continually increasing annual trend. The most productive country and collaboration was the United States. The top research hotspot is PD cognitive disorder. NMBM and artificial intelligence medical imaging have been applied in the clinical diagnosis, differential diagnosis, treatment, and prognosis of PD. The trends in this field include research on T1 weighted structure magnetic resonance imaging in accordance with voxel-based morphometry, PD cognitive disorder, and neuroimaging features of Lewy body dementia and Alzheimer disease.Conclusion:The development of NMBM in PD will be effectively promoted by drawing on international research hotspots and cutting-edge technologies, emphasizing international collaboration and institutional cooperation at the national level, and strengthening interdisciplinary research.

Artificial Intelligence-Based Low-Terahertz Imaging for Archaeological Shards’ Classification

In order to map the migration and introduction of farming into Europe during the seventh and sixth millennia Before Common Era, archeologists have made a connection between the study of pottery and farming migration. We are interested here in the classification of pottery into coiling and spiral types based on their manufacturing techniques. To distinguish between these two techniques, we look for the lines formed by air bubbles embedded in the pottery samples. Current methods make use of bulky systems, such as computerized tomography scanners or synchrotrons. Microwave acquisition and processing offer an interesting alternative, due to the possibility to have compact and portable systems. In this article, we investigate the classification of pottery based on low-terahertz measurements in the D-band. We process the measurements with 3-D fast Fourier transform. The resulting matrix is classified with an artificial neural network, multilayer perceptron, which is optimized with the gray wolf optimizer, a bioinspired algorithm. The first results show that the accuracy reaches up to 99% using all the acquired spatial and frequency measurements. Then, we optimize the millimeter-wave (mm-Wave) measurement system with a critical criterion on accuracy in two different scenarios. In the first scenario, we reduce the spatial acquisition but maintain the wideband operation and the results show that the accuracy is between 85% and 96%. In the second one, we reduce the spatial acquisition and use a single frequency. For this second scenario, we achieve a classification accuracy, which is between 77% and 100%.

Progress of artificial intelligence in imaging for the diagnosis of drug-resistant pulmonary tuberculosis

Recent technical advances have led to the application of artificial intelligence in many areas of medical science. This approach was applied early on to medical imaging, which involves a large amount of data for diagnosis. The application of artificial intelligence and imaging diagnostics for disease screening, diagnosis, and prognosis prediction is an area of active research. Early diagnosis and effective management of drug-resistant pulmonary tuberculosis (TB) can effectively control the spread of Mycobacterium TB, reduce hospitalization, and improve prognosis. We review the progress of artificial intelligence in assisting imaging-based diagnosis of this disease, and we offer useful perspectives on future research in this area.

Research progress of artificial intelligence-based imaging diagnosis of infectious diseases

With the rapid development and progress of theory and technology, artificial intelligence (AI) has overcome many early technical limitations. Remarkable advances have recently been made in the application of AI to various areas of health care, and improvements in the performance of computer-aided diagnostics, such as accuracy, specificity, and processing speed, have been achieved, especially in the classification and identification of lesions. We review the various applications and worldwide progress of AI-based imaging diagnosis of infectious diseases.

Abstract 1686: Characterizing tumor heterogeneity through label-free, morphology-based live cell sorting

Abstract The complexity of cancer is compounded by the continuous emergence of malignant cell populations that drive tumor heterogeneity and challenges diagnostic and therapeutic advances. Methods to study cancer at the single cell level are important to better understand tumor heterogeneity. Isolation of tumor cells from tissue typically relies on targeting biomarkers overexpressed in tumor cells, such as EpCAM, resulting in cells that may express common markers with morphologic and molecular heterogeneity. We developed a platform termed Computational Sorting and Mapping of Single cells (COSMOS) that uses high-dimensional morphology analysis of single cells using artificial intelligence, microfluidics and high resolution bright-field imaging to identify and enrich target cells. We used COSMOS to train a deep convolutional neural network (ConvNet) classifier to identify and enrich malignant cells from non-small cell lung cancer (NSCLC) dissociated tumor cell (DTC) samples. The enriched population of NSCLC cells are label-free, unperturbed and viable, making them amenable to many downstream analysis methods but importantly also contain single cell high-dimensional morphology profiles. We verified enrichment of malignant cells by performing RNA and DNA analysis on the enriched sample. Single cell RNA-seq (scRNA-Seq) analysis shows high levels of EpCAM expression in sorted cells. Copy number variation (CNV) analysis demonstrated increased amplitude of deletion and amplification peaks relative to the pre-sorted DTC sample. Further, mutational analysis show increased allele frequency of mutations including P53 and KRAS in post-sorted compared to pre-sorted samples. Morphological heterogeneity within the malignant population is observed by UMAP analysis with the presence of multiple clusters of morphologically unique tumor cell populations detected. We further trained the classifier to detect and enrich for each subpopulation; CNV, mutation, bulk RNASeq, and scRNA-Seq analysis revealed molecular differences between the morphology subgroups. Further work is planned to evaluate the link between the morphological, molecular and functional characteristics of each subpopulation. We demonstrate a platform that can enrich malignant cells based on morphology, yielding a population of cells that are label-free, viable and unperturbed, making them compatible with downstream molecular assays commonly used to study heterogeneity. Additionally the cells can be morphologically profiled at the single cell level yielding a high-dimensional morphological profile that reveals heterogeneous populations of cells that can be further characterized by molecular analysis and could offer a new dimension to understand heterogeneity and biomarker discovery. Citation Format: Andreja Jovic, Kiran Saini, Michael Phelan, Ryan Chow, Simo Zhang, Chassidy Johnson, Nianzhen Li, Thomas J. Musci, Mahyar Salek, Maddison (Mahdokht) Masaeli. Characterizing tumor heterogeneity through label-free, morphology-based live cell sorting [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1686.

Robust artificial intelligence-powered imaging biomarker based on mammography for risk prediction of breast cancer.

10533 Background: Accurate risk assessment allows the precise personalized screening of breast cancer. Conventional statistical risk models have been used that estimate the probability of breast cancer incidence with patient demographics, detailed personal and family history. The purpose of this study was to investigate the feasibility of the AI-powered Imaging Biomarker in mammography (IBM) which was developed to predict the risk of future breast cancer beyond the mammographic breast density. Methods: We trained and developed the deep learning risk model, the AI-powered IBM, using a total of 36,955 examinations from 21,438 patients, who underwent at least one mammogram using Hologic or Siemens machines and pathology-confirmed breast cancer outcomes. To discover the feasibility of the AI-powered IBM, mammograms and various clinical information including pathology-confirmed breast cancer outcomes were collected, which were only used for external validation. C-indices and receiver operating characteristic (ROC) curves for 1- to 5-year outcomes were obtained. We compared 5-year ROC area under the curves (AUCs) of our AI-powered IBM and statistical risk models including the Tyrer-Cuzick model and the Gail model, which were most commonly used and widely known, using DeLong’s test. Results: A total of 16,894 mammograms were collected for external validation, of which 4,002 were followed by a cancer diagnosis within 5 years. Our AI-powered IBM obtained C-index of 0.758, and the model demonstrated the risk of breast cancer with AUC of 0.895 (95% CI: 0.880, 0.909) at 1-year, 0.839 (0.824, 0.852) at 2-year, 0.807 (0.794, 0.819) at 3-year, 0.783 (0.7947, 0.819) at 4-year. The 5-year AUC of our AI-powered IBM was 0.808 (0.792, 0.822). Our AI-powered IBM showed significantly higher 5-year AUC than the Gail model (AUC: 0.572, P< 0.001) and the Tyrer-Cuzick model (0.569, P< 0.001). Conclusions: A deep learning AI-powered IBM using mammograms has a substantial potential to advance toward the robust risk prediction of breast cancer over conventional risk models. This approach for risk stratification of breast cancer might be feasible to improve personalized screening programs.

Application of informatics in cancer research and clinical practice: Opportunities and challenges.

Cancer informatics has significantly progressed in the big data era. We summarize the application of informatics approaches to the cancer domain from both the informatics perspective (e.g., data management and data science) and the clinical perspective (e.g., cancer screening, risk assessment, diagnosis, treatment, and prognosis). We discuss various informatics methods and tools that are widely applied in cancer research and practices, such as cancer databases, data standards, terminologies, high-throughput omics data mining, machine-learning algorithms, artificial intelligence imaging, and intelligent radiation. We also address the informatics challenges within the cancer field that pursue better treatment decisions and patient outcomes, and focus on how informatics can provide opportunities for cancer research and practices. Finally, we conclude that the interdisciplinary nature of cancer informatics and collaborations are major drivers for future research and applications in clinical practices. It is hoped that this review is instrumental for cancer researchers and clinicians with its informatics-specific insights.

Artificial Intelligence-Based CT Imaging on Diagnosis of Patients with Lumbar Disc Herniation by Scalpel Treatment.

The aim of this study was to explore the application effect of computed tomography (CT) image based on active contour segmentation algorithm in the treatment of lumbar disc herniation (LDH) with scalpel. 78 patients with LDH were selected and divided into a lateral crypt block treatment group (group A) and a scalpel treatment group (group B) randomly. All the patients were examined by lumbar CT images based on artificial intelligence (AI) algorithm. Then, the clinical efficacy and Japanese orthopedic association (JOA) and visual analogue scale (VAS) scores were compared between the two groups. It was found that the total effective rate in group B was higher (92.31% vs. 84.62%) (P < 0.05). After treatment, the disc height (DH) in group A was obviously lower, and the vertebral body slippage was obviously higher (P < 0.05) than before. After treatment, there were more patients with nerve root location changes, edema, or disappearance in group B (P < 0.05). In contrast with JOA and VAS scores before treatment, both the groups showed obvious differences after treatment, especially group B (P < 0.05). Therefore, the CT images based on the AI algorithm can be used to analyze the treatment effect of LDH, and the scalpel treatment was more effective.

Unsupervised clustering of Roman potsherds via Variational Autoencoders

In this paper we propose an artificial intelligence imaging solution to support archaeologists in the classification task of Roman commonware potsherds. Usually, each potsherd is represented by its sectional profile as a two dimensional black–white image and printed in archaeological books related to specific archaeological excavations. The partiality and handcrafted variance of the fragments make their matching a challenging problem: we propose to pair similar profiles via the unsupervised hierarchical clustering of non-linear features learned in the latent space of a deep convolutional Variational Autoencoder (VAE) network. Our contribution also include the creation of a ROman COmmonware POTtery (ROCOPOT) database, with more than 4000 potsherds profiles extracted from 25 Roman pottery corpora, and a MATLAB GUI software for the easy inspection of shape similarities. Results are commented both from a mathematical and archaeological perspective so as to unlock new research directions in both communities.

Nanotechnology in Kidney and Islet Transplantation: An Ongoing, Promising Field.

Organ transplantation has evolved rapidly in recent years as a reliable option for patients with end-stage organ failure. However, organ shortage, surgical risks, acute and chronic rejection reactions and long-term immunosuppressive drug applications and their inevitable side effects remain extremely challenging problems. The application of nanotechnology in medicine has proven highly successful and has unique advantages for diagnosing and treating diseases compared to conventional methods. The combination of nanotechnology and transplantation brings a new direction of thinking to transplantation medicine. In this article, we provide an overview of the application and progress of nanotechnology in kidney and islet transplantation, including nanotechnology for renal pre-transplantation preservation, artificial biological islets, organ imaging and drug delivery.

Preface

ESCUELA POLITÉCNICA NACIONAL Departamento de Física XVII Encuentro de Física October 2021Dear Participants,On behalf of the organizing committee, I would like to express our gratitude for sharing and discussing the results of your research in the framework of the XVII Encuentro de Física.October 2021 was an exciting month, in which we organized five practical workshops prior to the conference. The topics for each of these workshops were mechanics, electromagnetism, optics, biophysics, scientific programming, and the teaching of physics. All in all, more than 170 students took part in these.During the conference, we had 13 truly outstanding keynote speakers, 52 oral presentations, and 26 poster contributions. In this edition of the XVII Encuentro de Física we decided to accept works from all the major topics of physics reflecting the interests of our Physics Department. In Fig. 1, the topics distributions of the presented works as oral contributions and posters are shown.List of titles WELCOME TO THE XVII ENCUENTRO DE FíSICA, HISTORY OF THE ENCUENTRO DE FíSICA, COMMITTEE, KEYNOTES, ASTRONOMY, HIGH ENERGY PHYSICS, DARK MATTER AND STANDARD MODEL, ELECTROWEAK INTERACTIONS: FROM DISCOVERYTO PRECISION TESTS OF THE STANDARD MODEL, MACHINE LEARNING / COMPLEX SYSTEMS ARTIFICIAL INTELLIGENCE FROM PHYSICS, COMPLEX NETWORKS AND THEIR APPLICATIONS, ARTIFICIAL INTELLIGENCE IN MULTIMODALITY MEDICAL IMAGING: PAST, PRESENT, ANY FUTURE?, GEL DOSIMETRY, APPLICATIONS OF MACHINE LEARNING IN RADIOTHERAPY, LOW-COST METHODS AND INSTRUMENTS FOR ADVANCED HOLOGRAPHIC MICROSCOPY, TEACHING OF PHYSICS, TEACHING OF PHYSICS, LOW-COST EXPERIMENTAL RESOURCES IN THE TEACHING OF PHYSICS TO SHARE WITH TEACHERS, PEROVSKITE SOLAR CELLS, A BRIEF LOOK INTO ELECTRON MICROSCOPY: UNVEILING MATERIALS PROPERTIES AT THE ATOMIC AND NANOMETER LEVEL, A VIRTUAL TOUR OF THE GALAPAGOS ISLANDS, ORAL CONTRIBUTIONS, Complex systems, Condensed matter physics, High-energy physics, Medical physics, Optics, Teaching of physics, Other, POSTERS, Complex systems, Condensed matter physics, High-energy physics, Optics, Other are available in this Pdf.

The Value of Artificial Intelligence-Assisted Imaging in Identifying Diagnostic Markers of Sarcopenia in Patients with Cancer.

Sarcopenia is defined as the loss of skeletal muscle mass and muscle function. It is common in patients with malignancies and often associated with adverse clinical outcomes. The presence of sarcopenia in patients with cancer is determined by body composition, and recently, radiologic technology for the accurate estimation of body composition is under development. Artificial intelligence- (AI-) assisted image measurement facilitates the detection of sarcopenia in clinical practice. Sarcopenia is a prognostic factor for patients with cancer, and confirming its presence helps to recognize those patients at the greatest risk, which provides a guide for designing individualized cancer treatments. In this review, we examine the recent literature (2017-2021) on AI-assisted image assessment of body composition and sarcopenia, seeking to synthesize current information on the mechanism and the importance of sarcopenia, its diagnostic image markers, and the interventions for sarcopenia in the medical care of patients with cancer. We concluded that AI-assisted image analysis is a reliable automatic technique for segmentation of abdominal adipose tissue. It has the potential to improve diagnosis of sarcopenia and facilitates identification of oncology patients at the greatest risk, supporting individualized prevention planning and treatment evaluation. The capability of AI approaches in analyzing series of big data and extracting features beyond manual skills would no doubt progressively provide impactful information and greatly refine the standard for assessing sarcopenia risk in patients with cancer.

Diagnostic Value of Artificial Intelligence Based on CT Image in Benign and Malignant Pulmonary Nodules.

Objective To evaluate the diagnostic value of artificial intelligence-assisted CT imaging in benign and malignant pulmonary nodules. Methods The CT scan screening of pulmonary nodules from November 2018 to November 2020 was retrospectively collected. The diagnosis of pulmonary nodules and surgical treatment were performed. A total of 194 nodules in 152 patients with clear pathological results were observed. All patients underwent CT examination to analyze the consistency of the results of artificial intelligence, physician reading according to imaging features, multidisciplinary team work (MDT) diagnosis, and postoperative pathological results; the diagnostic efficacy of different diagnostic methods for solitary pulmonary nodules and the differences of ROC curve and AUC were analyzed. The accuracy, specificity, sensitivity, positive predictive value, negative predictive value, false negative rate, and false positive rate of different diagnostic methods for pulmonary nodules were calculated, and the ROC curves of different diagnostic methods were plotted. Results The accuracy, sensitivity, specificity, and Youden index of artificial intelligence (AI) were 89.69%, 92.98%, 65.22%, and 58.20%; the accuracy, sensitivity, specificity, and Youden index of physician reading were 85.57%, 88.30%, 65.22%, and 53.52%; the accuracy, sensitivity, specificity, and Youden index of MDT were 96.91%, 98.25%, 86.96%, and 85.21%, respectively. The kappa values of artificial intelligence, physician reading, and MDT were 0.541, 0.437, and 0.852, and the AUC was 0.768, 0.791, and 0.926, respectively (P < 0.001). The average detection time of pulmonary nodules in the AI group, manual reading group, and MAT group was (145 ± 97) s, (534 ± 297) s, and (421 ± 128) s (P < 0.001). Conclusion Artificial intelligence pulmonary nodule detection system can improve the coincidence rate and accuracy of early diagnosis of lung cancer, shorten the average detection time, and provide more accurate information for clinical decision-making.

State-of-the-art review of lung imaging in cystic fibrosis with recommendations for pulmonologists and radiologists from the "iMAging managEment of cySTic fibROsis" (MAESTRO) consortium.

Imaging represents an important noninvasive means to assess cystic fibrosis (CF) lung disease, which remains the main cause of morbidity and mortality in CF patients. While the development of new imaging techniques has revolutionised clinical practice, advances have posed diagnostic and monitoring challenges. The authors aim to summarise these challenges and make evidence-based recommendations regarding imaging assessment for both clinicians and radiologists. A committee of 21 experts in CF from the 10 largest specialist centres in Italy was convened, including a radiologist and a pulmonologist from each centre, with the overall aim of developing clear and actionable recommendations for lung imaging in CF. An a priori threshold of at least 80% of the votes was required for acceptance of each statement of recommendation. After a systematic review of the relevant literature, the committee convened to evaluate 167 articles. Following five RAND conferences, consensus statements were developed by an executive subcommittee. The entire consensus committee voted and approved 28 main statements. There is a need for international guidelines regarding the appropriate timing and selection of imaging modality for patients with CF lung disease; timing and selection depends upon the clinical scenario, the patient's age, lung function and type of treatment. Despite its ubiquity, the use of the chest radiograph remains controversial. Both computed tomography and magnetic resonance imaging should be routinely used to monitor CF lung disease. Future studies should focus on imaging protocol harmonisation both for computed tomography and for magnetic resonance imaging. The introduction of artificial intelligence imaging analysis may further revolutionise clinical practice by providing fast and reliable quantitative outcomes to assess disease status. To date, there is no evidence supporting the use of lung ultrasound to monitor CF lung disease.

Multi-center evaluation of artificial intelligent imaging and clinical models for predicting neoadjuvant chemotherapy response in breast cancer.

Neoadjuvant chemotherapy (NAC) plays an important role in the management of locally advanced breast cancer. It allows for downstaging of tumors, potentially allowing for breast conservation. NAC also allows for in-vivo testing of the tumors' response to chemotherapy and provides important prognostic information. There are currently no clearly defined clinical models that incorporate imaging with clinical data to predict response to NAC. Thus, the aim of this work is to develop a predictive AI model based on routine CT imaging and clinical parameters to predict response to NAC. The CT scans of 324 patients with NAC from multiple centers in Singapore were used in this study. Four different radiomics models were built for predicting pathological complete response (pCR): first two were based on textural features extracted from peri-tumoral and tumoral regions, the third model based on novel space-resolved radiomics which extract feature maps using voxel-based radiomics and the fourth model based on deep learning (DL). Clinical parameters were included to build a final prognostic model. The best performing models were based on space-resolved and DL approaches. Space-resolved radiomics improves the clinical AUCs of pCR prediction from 0.743 (0.650 to 0.831) to 0.775 (0.685 to 0.860) and our DL model improved it from 0.743 (0.650 to 0.831) to 0.772 (0.685 to 0.853). The tumoral radiomics model performs the worst with no improvement of the AUC from the clinical model. The peri-tumoral combined model gives moderate performance with an AUC of 0.765 (0.671 to 0.855). Radiomics features extracted from diagnostic CT augment the predictive ability of pCR when combined with clinical features. The novel space-resolved radiomics and DL radiomics approaches outperformed conventional radiomics techniques.

Artificial Intelligence Algorithm-Based Intraoperative Magnetic Resonance Navigation for Glioma Resection.

The study aimed to analyze the application value of artificial intelligence algorithm-based intraoperative magnetic resonance imaging (iMRI) in neurosurgical glioma resection. 108 patients with glioma in a hospital were selected and divided into the experimental group (intraoperative magnetic resonance assisted glioma resection) and the control group (conventional surgical experience resection), with 54 patients in each group. After the resection, the tumor resection rate, NIHSS (National Institute of Health Stroke Scale) score, Karnofsky score, and postoperative intracranial infection were calculated in the two groups. The results revealed that the average tumor resection rate in the experimental group was significantly higher than that in the control group (P < 0.05). There was no significant difference in Karnofsky score before and after the operation in the experimental group (P > 0.05). There was no significant difference in NIHSS score between the experimental group and the control group after resection (P > 0.05). The number of patients with postoperative neurological deficits in the experimental group was smaller than that in the control group. In addition, there was no significant difference in infection rates between the two groups after glioma resection (P > 0.05). In summary, intraoperative magnetic resonance navigation on the basis of a segmentation dictionary learning algorithm has great clinical value in neurosurgical glioma resection. It can maximize the removal of tumors and ensure the integrity of neurological function while avoiding an increased risk of postoperative infection, which is of great significance for the treatment of glioma.

Artificial intelligence and imaging: Opportunities in cardio-oncology

Cardiovascular disease is a leading cause of death in cancer survivors. It is critical to apply new predictive and early diagnostic methods in this population, as this can potentially inform cardiovascular treatment and surveillance decision-making. We discuss the application of artificial intelligence (AI) technologies to cardiovascular imaging in cardio-oncology, with a particular emphasis on prevention and targeted treatment of a variety of cardiovascular conditions in cancer patients. Recently, the use of AI-augmented cardiac imaging in cardio-oncology is gaining traction. A large proportion of cardio-oncology patients are screened and followed using left ventricular ejection fraction (LVEF) and global longitudinal strain (GLS), currently obtained using echocardiography. This use will continue to increase with new cardiotoxic cancer treatments. AI is being tested to increase precision, throughput, and accuracy of LVEF and GLS, guide point-of-care image acquisition, and integrate imaging and clinical data to optimize the prediction and detection of cardiac dysfunction. The application of AI to cardiovascular magnetic resonance imaging (CMR), computed tomography (CT; especially coronary artery calcium or CAC scans), single proton emission computed tomography (SPECT) and positron emission tomography (PET) imaging acquisition is also in early stages of analysis for prediction and assessment of cardiac tumors and cardiovascular adverse events in patients treated for childhood or adult cancer. The opportunities for application of AI in cardio-oncology imaging are promising, and if availed, will improve clinical practice and benefit patient care.