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| 1 | Find medicine and pharmaceuticals online-an overview of Chinese and foreign medical search engine显示文摘 | XIA X FANG P | 2000 | Journal of Medical Intelligence2000，21，4： | 1 |
| 2 | A System for Mela- noma Diagnosis Based on Data Mining显示文摘 | Grzymala-Busse J W Hippe Z S Piatek L | 2013 | Medical Applications of Artificial Intelligence2013，，： | 1 |
| 3 | Classification of the 12-lead electrocardiogram employing a framework of bigroup neural networks显示文摘 | Nugent C D Web J A C Black N D | 1998 | Intelligent Methods in Healthcare and Medical Applications1998，514，6： | 1 |
| 4 | Enhancing medical-imaging artificial intelligence through holistic use of time-tested key imaging and clinical parameters:Future insights显示文摘Much of the published literature in Radiology-related Artificial Intelligence(AI)focuses on single tasks,such as identifying the presence or absence or severity of specific lesions.Progress comparable to that achieved for general-purpose computer vision has been hampered by the unavailability of large and diverse radiology datasets containing different types of lesions with possibly multiple kinds of abnormalities in the same image.Also,since a diagnosis is rarely achieved through an image alone,radiology AI must be able to employ diverse strategies that consider all available evidence,not just imaging information.Using key imaging and clinical signs will help improve their accuracy and utility tremendously.Employing strategies that consider all available evidence will be a formidable task;we believe that the combination of human and computer intelligence will be superior to either one alone.Further,unless an AI application is explainable,radiologists will not trust it to be either reliable or bias-free;we discuss some approaches aimed at providing better explanations,as well as regulatory concerns regarding explainability(“transparency”).Finally,we look at federated learning,which allows pooling data from multiple locales while maintaining data privacy to create more generalizable and reliable models,and quantum computing,still prototypical but potentially revolutionary in its computing impact. | Prakash Nadkarni Suleman Adam Merchant | 2022 | Artificial Intelligence in Medical Imaging2022，3，3： | 1 |
| 5 | Characterization of signals from multiscale edges显示文摘 | MALLAT S ZHONG S | 1992 | IEEE Transactions on Pattern Analysis and Medical Intelligence1992，14，7： | 1 |
| 6 | Cardiac arrest during spinal anesthesia: co mmon mechanisms and strategies for prevention 显示文摘 | Pollard JB | 2000 | Medical Intelligence2000，92，1： | 1 |
| 7 | Artificial intelligence in radiation oncology显示文摘Artificial intelligence(AI)is a computer science that tries to mimic human-like intelligence in machines that use computer software and algorithms to perform specific tasks without direct human input.Machine learning(ML)is a subunit of AI that uses data-driven algorithms that learn to imitate human behavior based on a previous example or experience.Deep learning is an ML technique that uses deep neural networks to create a model.The growth and sharing of data,increasing computing power,and developments in AI have initiated a transformation in healthcare.Advances in radiation oncology have produced a significant amount of data that must be integrated with computed tomography imaging,dosimetry,and imaging performed before each fraction.Of the many algorithms used in radiation oncology,has advantages and limitations with different computational power requirements.The aim of this review is to summarize the radiotherapy(RT)process in workflow order by identifying specific areas in which quality and efficiency can be improved by ML.The RT stage is divided into seven stages:patient evaluation,simulation,contouring,planning,quality control,treatment application,and patient follow-up.A systematic evaluation of the applicability,limitations,and advantages of AI algorithms has been done for each stage. | Melek Yakar Durmus Etiz | 2021 | Artificial Intelligence in Medical Imaging2021，2，2： | 0 |
| 8 | Intrathyroidal ectopic thymus:Ultrasonographic features and differential diagnosis显示文摘Intrathyroidal ectopic thymus(IET)is defined as an ectopic thymus tissue that is generally found incidentally and rarely in the thyroid gland in the pediatric group.It occurs as a result of disruption of the embryological migration path and the settling of the thymus tissue into the thyroid gland.In the differential diagnosis,it is mostly confused with thyroid nodules.Although thyroid nodules are less common in children than adults,the rate of malignancy is much higher.Therefore,knowing the general ultrasound findings of IET better may prevent unnecessary invasive attempts and surgical procedures.In this article,we tried to compile the key imaging findings of IET. | Erdal Karavas Oguzhan Tokur Sonay Aydin Dilek Gokharman Cigdem Uner | 2021 | Artificial Intelligence in Medical Imaging2021，2，2： | 0 |
| 9 | Current landscape and potential future applications of artificial intelligence in medical physics and radiotherapy显示文摘Artificial intelligence(AI)has seen tremendous growth over the past decade and stands to disrupts the medical industry.In medicine,this has been applied in medical imaging and other digitised medical disciplines,but in more traditional fields like medical physics,the adoption of AI is still at an early stage.Though AI is anticipated to be better than human in certain tasks,with the rapid growth of AI,there is increasing concerns for its usage.The focus of this paper is on the current landscape and potential future applications of artificial intelligence in medical physics and radiotherapy.Topics on AI for image acquisition,image segmentation,treatment delivery,quality assurance and outcome prediction will be explored as well as the interaction between human and AI.This will give insights into how we should approach and use the technology for enhancing the quality of clinical practice. | Wing-Yan Ip Fu-Ki Yeung Shang-Peng Felix Yung Hong-Cheung Jeffrey Yu Tsz-Him So Varut Vardhanabhuti | 2021 | Artificial Intelligence in Medical Imaging2021，2，2： | 0 |
| 10 | Current trends of artificial intelligence in cancer imaging显示文摘In this editorial,we discussed the current research status of artificial intelligence(AI)in Oncology,reviewing the basics of machine learning(ML)and deep learning(DL)techniques and their emerging applications on clinical and imaging cancer workflow.The growing amounts of available“big data”coupled to the increasing computational power have enabled the development of computerbased systems capable to perform advanced tasks in many areas of clinical care,especially in medical imaging.ML is a branch of data science that allows the creation of computer algorithms that can learn and make predictions without prior instructions.DL is a subgroup of artificial neural network algorithms configurated to automatically extract features and perform high-level tasks;convolutional neural networks are the most common DL models used in medical image analysis.AI methods have been proposed in many areas of oncology granting promising results in radiology-based clinical applications.In detail,we explored the emerging applications of AI in oncological risk assessment,lesion detection,characterization,staging,and therapy response.Critical issues such as the lack of reproducibility and generalizability need to be addressed to fully implement AI systems in clinical practice.Nevertheless,AI impact on cancer imaging has been driving the shift of oncology towards a precision diagnostics and personalized cancer treatment. | Francesco Verde Valeria Romeo Arnaldo Stanzione Simone Maurea | 2020 | Artificial Intelligence in Medical Imaging2020，1，3： | 0 |
| 11 | Predicting a live birth by artificial intelligence incorporating both the blastocyst image and conventional embryo evaluation parameters显示文摘BACKGROUND The achievement of live birth is the goal of assisted reproductive technology in reproductive medicine.When the selected blastocyst is transferred to the uterus,the degree of implantation of the blastocyst is evaluated by microscopic inspection,and the result is only about 30%-40%,and the method of predicting live birth from the blastocyst image is unknown.Live births correlate with several clinical conventional embryo evaluation parameters(CEE),such as maternal age.Therefore,it is necessary to develop artificial intelligence(AI)that combines blastocyst images and CEE to predict live births.AIM To develop an AI classifier for blastocyst images and CEE to predict the probability of achieving a live birth.METHODS A total of 5691 images of blastocysts on the fifth day after oocyte retrieval obtained from consecutive patients from January 2009 to April 2017 with fully deidentified data were retrospectively enrolled with explanations to patients and a website containing additional information with an opt-out option.We have developed a system in which the original architecture of the deep learning neural network is used to predict the probability of live birth from a blastocyst image and CEE.RESULTS The live birth rate was 0.387(=1587/4104 cases).The number of independent clinical information for predicting live birth is 10,which significantly avoids multicollinearity.A single AI classifier is composed of ten layers of convolutional neural networks,and each elementwise layer of ten factors is developed and obtained with 42792 as the number of training data points and 0.001 as the L2 regularization value.The accuracy,sensitivity,specificity,negative predictive value,positive predictive value,Youden J index,and area under the curve values for predicting live birth are 0.743,0.638,0.789,0.831,0.573,0.427,and 0.740,respectively.The optimal cut-off point of the receiver operator characteristic curve is 0.207.CONCLUSION AI classifiers have the potential of predicting live births that humans cannot predict.Artificial intelligence may make progress in assisted reproductive technology. | Yasunari Miyagi Toshihiro Habara Rei Hirata Nobuyoshi Hayashi | 2020 | Artificial Intelligence in Medical Imaging2020，1，3： | 0 |
| 12 | New Year's greeting and overview of Artificial Intelligence in Medical Imaging in 2021显示文摘As editors of Artificial Intelligence in Medical Imaging(AIMI),it is our great pleasure to take this opportunity to wish all of our authors,subscribers,readers,Editorial Board members,independent expert referees,and staff of the Editorial Office a Very Happy New Year.On behalf of the Editorial Team,we would like to express our gratitude to all of the authors who have contributed their valuable manuscripts,our independent referees,and our subscribers and readers for their continuous support,dedication,and encouragement.Together with an excellent of team effort by our Editorial Board members and staff of the Editorial Office,AIMI advanced in 2020 and we look forward to greater achievements in 2021. | Yun-Xiaojian Jun Shen | 2021 | Artificial Intelligence in Medical Imaging2021，2，1： | 0 |
| 13 | Artificial intelligence in ophthalmology: A new era is beginning显示文摘The use of artificial intelligence(AI)in ophthalmology is not very new and its use is expanding into various subspecialties of the eye like retina and glaucoma,thereby helping ophthalmologists to diagnose and treat diseases better than before.Incorporating“deep learning”(a subfield of AI)into image-based systems such as optical coherence tomography has dramatically improved the machine's ability to screen and identify stages of diabetic retinopathy accurately.Similar applications have been tried in the field of retinopathy of prematurity and agerelated macular degeneration,a silent retinal condition that needs to be diagnosed early to prevent progression.The advent of AI into glaucoma diagnostics in analyzing visual fields and assessing disease progression also holds a promising role.The ability of the software to detect even a subtle defect that the human eye can miss has led to a revolution in the management of certain ocular conditions.However,there are few significant challenges in the AI systems,such as the incorporation of quality images,training sets and the black box dilemma.Nevertheless,despite the existing differences,there is always a chance of improving the machines/software to potentiate their efficacy and standards.This review article shall discuss the current applications of AI in ophthalmology,significant challenges and the prospects as to how both science and medicine can work together. | Bijnya Birajita Panda Subhodeep Thakur Sumita Mohapatra Subhabrata Parida | 2021 | Artificial Intelligence in Medical Imaging2021，2，1： | 0 |
| 14 | Rising role of artificial intelligence in image reconstruction for biomedical imaging显示文摘In this editorial,we review recent progress on the applications of artificial intelligence(AI)in image reconstruction for biomedical imaging.Because it abandons prior information of traditional artificial design and adopts a completely data-driven mode to obtain deeper prior information via learning,AI technology plays an increasingly important role in biomedical image reconstruction.The combination of AI technology and the biomedical image reconstruction method has become a hotspot in the field.Favoring AI,the performance of biomedical image reconstruction has been improved in terms of accuracy,resolution,imaging speed,etc.We specifically focus on how to use AI technology to improve the performance of biomedical image reconstruction,and propose possible future directions in this field. | Xue-Li Chen Tian-Yu Yan Nan Wang Karen M von Deneen | 2020 | Artificial Intelligence in Medical Imaging2020，1，1： | 0 |
| 15 | Breast dynamic contrast-enhanced-magnetic resonance imaging and radiomics:State of art显示文摘Breast cancer represents the most common malignancy in women,being one of the most frequent cause of cancer-related mortality.Ultrasound,mammography,and magnetic resonance imaging(MRI)play a pivotal role in the diagnosis of breast lesions,with different levels of accuracy.Particularly,dynamic contrastenhanced MRI has shown high diagnostic value in detecting multifocal,multicentric,or contralateral breast cancers.Radiomics is emerging as a promising tool for quantitative tumor evaluation,allowing the extraction of additional quantitative data from radiological imaging acquired with different modalities.Radiomics analysis may provide novel information through the quantification of lesions heterogeneity,that may be relevant in clinical practice for the characterization of breast lesions,prediction of tumor response to systemic therapies and evaluation of prognosis in patients with breast cancers.Several published studies have explored the value of radiomics with good-to-excellent diagnostic and prognostic performances for the evaluation of breast lesions.Particularly,the integrations of radiomics data with other clinical and histopathological parameters have demonstrated to improve the prediction of tumor aggressiveness with high accuracy and provided precise models that will help to guide clinical decisions and patients management.The purpose of this article in to describe the current application of radiomics in breast dynamic contrast-enhanced MRI. | Alessia Orlando Mariangela Dimarco Roberto Cannella Tommaso Vincenzo Bartolotta | 2020 | Artificial Intelligence in Medical Imaging2020，1，1： | 0 |
| 16 | Artificial intelligence in pancreatic disease显示文摘In recent years,the application of artificial intelligence(AI)in radiology has been growing rapidly,fueled by the availability of large datasets,advances in computing power,and newly developed algorithms.Progress in AI applied to medical imaging analyses has transformed these images into quantitative data,termed radiomics.When combined with patients’clinical data,these models,when developed by machine learning,have the potential to improve diagnostic,prognostic,and predictive accuracy.Currently,limited literature is available on the use of radiomics for pancreatic disease.Here,we will review recent studies in the application of AI in a variety of pancreatic diseases,mainly involving lesion detection,tumor characterization,tumor grading,response,and prognosis evaluation.Finally,we will also discuss the challenges and prospects in the field of radiomics for pancreatic disease. | Bang-Bin Chen | 2020 | Artificial Intelligence in Medical Imaging2020，1，1： | 0 |
| 17 | Machine learning for diagnosis of coronary artery disease in computed tomography angiography:A survey显示文摘Coronary artery disease(CAD)has become a major illness endangering human health.It mainly manifests as atherosclerotic plaques,especially vulnerable plaques without obvious symptoms in the early stage.Once a rupture occurs,it will lead to severe coronary stenosis,which in turn may trigger a major adverse cardiovascular event.Computed tomography angiography(CTA)has become a standard diagnostic tool for early screening of coronary plaque and stenosis due to its advantages in high resolution,noninvasiveness,and three-dimensional imaging.However,manual examination of CTA images by radiologists has been proven to be tedious and time-consuming,which might also lead to intra-and interobserver errors.Nowadays,many machine learning algorithms have enabled the(semi-)automatic diagnosis of CAD by extracting quantitative features from CTA images.This paper provides a survey of these machine learning algorithms for the diagnosis of CAD in CTA images,including coronary artery extraction,coronary plaque detection,vulnerable plaque identification,and coronary stenosis assessment.Most included articles were published within this decade and are found in the Web of Science.We wish to give readers a glimpse of the current status,challenges,and perspectives of these machine learning-based analysis methods for automatic CAD diagnosis. | Feng-Jun Zhao Si-Qi Fan Jing-Fang Ren Karen M von Deneen Xiao-Wei He Xue-Li Chen | 2020 | Artificial Intelligence in Medical Imaging2020，1，1： | 0 |
| 18 | Acute pancreatitis:A pictorial review of early pancreatic fluid collections显示文摘Acute pancreatitis is a common acute inflammatory disease involving the pancreas and peripancreatic tissues or remote organs.The revised Atlanta classification 2012 of acute pancreatitis divides patients into mild,moderately severe and severe groups.Major changes of the classification include acute fluid collection terminology.However,some inappropriate terms of the radiological diagnosis reports in the daily clinical work or available literature may still be found.The aim of this review article is:to present an image-rich overview of different morphologic characteristics of the early-stage(within 4 wk after symptom onset)local complications associated with acute pancreatitis by computed tomography or magnetic resonance imaging;to clarify confusing imaging concepts for pancreatic fluid collections and underline standardised reporting nomenclature;to assist communication among treating physicians;and to facilitate the implications for clinical management decision-making. | Bo Xiao | 2020 | Artificial Intelligence in Medical Imaging2020，1，1： | 0 |
| 19 | Acoustic concept based on an autonomous capsule and a wideband concentric ring resonator for pathophysiological prevention显示文摘BACKGROUND Research on the performance of elements constituting our modern environment is constantly evolving,both on a daily basis and on technological basis.But to date,the response of the system to the expectations of the population remains too modest.AIM To elaborate an ultrasonic technique to scan and evaluate in-vivo physiological properties by coupling sensors and multilayer biological tissues model.METHODS A low-frequency ultrasonic method(around a frequency of 32 KHz)based on the use of an innovative autonomous ultrasonic capsule as a miniaturized elementary spherical sensor(1 cm of diameter)and micro-rings resonators were examined.RESULTS Other their functions as passive listeners for the prevention and diagnosis in physiopathology of the respiratory and laryngeal apparatus,these microresonators coupled to the ultrasonic capsule through biological tissues(the body)are capable of evaluating the effects of aggression of the environment on human metabolism.CONCLUSION This would allow consequently the detection of some potential diseases at an early stage,even in people who still represent no symptoms,which would permit an early treatment and a higher chance of cure. | Amina Medjdoub Fabrice Lefebvre Nadine Saad Saïd Soudani Georges Nassar | 2020 | Artificial Intelligence in Medical Imaging2020，1，1： | 0 |
| 20 | Cerebral amyloid angiopathy vs Alzheimer’s dementia:Diagnostic conundrum显示文摘BACKGROUND Diagnosis of a dementia subtype can be complex and often requires comprehensive cognitive assessment and dedicated neuroimaging.Clinicians are prone to cognitive biases when reviewing such images.We present a case of cognitive impairment and demonstrate that initial imaging may have resulted in misleading the diagnosis due to such cognitive biases.CASE SUMMARY A 76-year-old man with no cognitive impairment presented with acute onset word finding difficulty with unremarkable blood tests and neurological examination.Magnetic resonance imaging(MRI)demonstrated multiple foci of periventricular and subcortical microhaemorrhage,consistent with cerebral amyloid angiopathy(CAA).Cognitive assessment of this patient demonstrated marked impairment mainly in verbal fluency and memory.However,processing speed and executive function are most affected in CAA,whereas episodic memory is relatively preserved,unlike in other causes of cognitive impairment,such as Alzheimer’s dementia(AD).This raised the question of an underlying diagnosis of dementia.Repeat MRI with dedicated coronal views demonstrated mesial temporal lobe atrophy which is consistent with AD.CONCLUSION MRI brain can occasionally result in diagnostic overshadowing,and the application of artificial intelligence to medical imaging may overcome such cognitive biases. | Jamie Arberry Sarneet Singh Ruth Akiyo Mizoguchi | 2020 | Artificial Intelligence in Medical Imaging2020，1，1： | 0 |
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