Dental CT imaging: a look at the jaw

The jaw comprises two complex bony structures: the mandible and maxilla. Their curved or archlike configuration makes radiographic imaging difficult. Furthermore, the superimposition of dense teeth and roots may obscure underlying tissues, and streak artifacts from dental restorations often degrade computed tomographic (CT) images. Recently, dental CT reformatting programs that use thin transverse images of the jaw to reformat multiple panoramic and cross-sectional views were developed. Since images are reformatted, streak artifacts that degrade bone visualization at direct coronal CT are projected over the crowns of the teeth, permitting optimal viewing of bone. As a result, these programs have been successfully used to evaluate implants, cysts, tumors, and surgical procedures. They have created not only a new modality for viewing the jaw but also a new partnership between dentists and radiologists. Questions that cannot be answered in the dentist's office with conventional radiographs are now answered in radiology departments with dental CT scans. The objective of this article is to trace, with a series of examples, the effect that this imaging modality has had on radiographic evaluation of the jaw and to provide the reader with knowledge that should enable him or her to become more involved in imaging of the jaw.     

Return-to-play guidelines in concussion: a closer look at the literature

Traumatic brain injuries that occur during sports have gained significant attention in the literature. Despite improved education and research on proper identification, risk management, and treatment, standardized methods for returning an athlete to play after a concussion are lacking in universal applicability. Current return-to-play guidelines are considered appropriate for the majority of athletes who recover within a few weeks. However, applicability of such guidelines becomes difficult when treating those athletes who experience prolonged symptoms or do not have the resources available to adequately manage complex presentations of concussions. Understanding the guidelines with consideration to special populations will assist the treating physician in providing an appropriate and individualized evaluation and treatment plan to safely return an athlete with a concussion back to play without compromising his or her health.     

Computer-aided diagnosis and artificial intelligence in clinical imaging

Computer-aided diagnosis (CAD) is rapidly entering the radiology mainstream. It has already become a part of the routine clinical work for the detection of breast cancer with mammograms. The computer output is used as a "second opinion" in assisting radiologists' image interpretations. The computer algorithm generally consists of several steps that may include image processing, image feature analysis, and data classification via the use of tools such as artificial neural networks (ANN). In this article, we will explore these and other current processes that have come to be referred to as "artificial intelligence." One element of CAD, temporal subtraction, has been applied for enhancing interval changes and for suppressing unchanged structures (eg, normal structures) between 2 successive radiologic images. To reduce misregistration artifacts on the temporal subtraction images, a nonlinear image warping technique for matching the previous image to the current one has been developed. Development of the temporal subtraction method originated with chest radiographs, with the method subsequently being applied to chest computed tomography (CT) and nuclear medicine bone scans. The usefulness of the temporal subtraction method for bone scans was demonstrated by an observer study in which reading times and diagnostic accuracy improved significantly. An additional prospective clinical study verified that the temporal subtraction image could be used as a "second opinion" by radiologists with negligible detrimental effects. ANN was first used in 1990 for computerized differential diagnosis of interstitial lung diseases in CAD. Since then, ANN has been widely used in CAD schemes for the detection and diagnosis of various diseases in different imaging modalities, including the differential diagnosis of lung nodules and interstitial lung diseases in chest radiography, CT, and position emission tomography/CT. It is likely that CAD will be integrated into picture archiving and communication systems and will become a standard of care for diagnostic examinations in daily clinical work.     

医学影像人工智能新进展

随着人工智能（AI）与各个领域的结合,AI已经成为当今社会的研究热点。目前医疗行业人员的短缺及医学诊断准确率的提高使得AI在医疗行业的应用非常重要,尤其是医学影像诊断方面。AI辅助诊断将会提高疾病的检出率,为临床医师提供更有效的诊断和治疗信息,同时减少影像医师的重复工作,节省出更多的时间研究疑难病例。笔者简要介绍医学影像AI,结合国内外最新和最有影响力的研究成果,阐述医学影像AI的研究新进展。     

Three-dimensional techniques and artificial intelligence in thallium-201 cardiac imaging

Three-dimensional reconstruction techniques including bull's-eye polar-coordinate maps, surface rendering, and surface modeling have been developed that help interpreting physicians assimilate complex 3-D tomographic data. Comparison of patient data with normal files highlights myocardial perfusion abnormalities, thus facilitating their recognition. In addition, AI systems that use heuristically defined rules derived from an expert knowledge base assist inexperienced observers in drawing conclusions regarding scan abnormalities.     

人工智能在心脏影像诊断中的研究进展

人工智能(AI)中的传统机器学习和深度学习依赖于数据的训练来实现对疾病精确诊断及分类,在心脏影像诊断中各具优势。近年AI在心脏超声、CT、MRI、单光子发射体层成像(SPECT)和正电子发射体层成像(PET)中的应用以心脏分割和快速成像为核心,不断深入对低辐射剂量扫描、精确的自动化数据测量以及准确的预后评估的研究。就AI基本原理及基于AI的不同影像检查方法在心脏成像中的研究进展予以综述。     

积极稳妥地推进人工智能在医学影像的应用

医学影像人工智能（AI）被用于图像采集与质控、图像重建、后处理、病灶检测、辅助诊断、病情监测和结构化报告等方面。经历数年发展,一些医学影像AI软件已经投入使用,提升了阅片速度,减轻了医师负担,并减少漏诊。目前投入使用的医学影像AI以单一病变的检测为主,切换不同软件比较繁琐费时,多中心研究相对较少,许多模型有较高的偏倚风...     

人工智能在急性缺血性脑卒中成像中的应用进展

人工智能(AI)技术可采用多种算法模拟人类认知和信息处理过程,与CT和MRI相结合可用于急性缺血性脑卒中成像,包括梗死灶的检测、影像分割、头颈大血管闭塞的检测和病人预后预测等。采用AI技术分析或构建模型有助于临床医师对脑卒中病人的尽早诊疗、及时干预和随访评估。概述AI的概念,并就其在急性缺血性脑卒中病人CT平扫、CT血管成像(CTA)、CT灌注成像(CTP)、MRI中的应用进展进行综述。     

SARS-CoV-2 diagnosis using medical imaging techniques and artificial intelligence: A review

ObjectiveSARS-CoV-2 is a worldwide health emergency with unrecognized clinical features. This paper aims to review the most recent medical imaging techniques used for the diagnosis of SARS-CoV-2 and their potential contributions to attenuate the pandemic. Recent researches, including artificial intelligence tools, will be described.MethodsWe review the main clinical features of SARS-CoV-2 revealed by different medical imaging techniques. First, we present the clinical findings of each technique. Then, we describe several artificial intelligence approaches introduced for the SARS-CoV-2 diagnosis.ResultsCT is the most accurate diagnostic modality of SARS-CoV-2. Additionally, ground-glass opacities and consolidation are the most common signs of SARS-CoV-2 in CT images. However, other findings such as reticular pattern, and crazy paving could be observed. We also found that pleural effusion and pneumothorax features are less common in SARS-CoV-2. According to the literature, the B lines artifacts and pleural line irregularities are the common signs of SARS-CoV-2 in ultrasound images. We have also stated the different studies, focusing on artificial intelligence tools, to evaluate the SARS-CoV-2 severity. We found that most of the reported works based on deep learning focused on the detection of SARS-CoV-2 from medical images while the challenge for the radiologists is how to differentiate between SARS-CoV-2 and other viral infections with the same clinical features.ConclusionThe identification of SARS-CoV-2 manifestations on medical images is a key step in radiological workflow for the diagnosis of the virus and could be useful for researchers working on computer-aided diagnosis of pulmonary infections.     

基于影像组学的人工智能在脑胶质瘤 MRI诊断中的应用

影像组学能够从影像大数据中挖掘潜在的信息以利于实现精准医疗。基于影像组学的人工智能（AI）技术能实现计算机模拟人类思维,代替人工高效地进行数据挖掘。医学影像学的AI技术能有效辅助放射科医生对脑胶质瘤的MR影像诊断。综述基于影像组学的AI技术在脑胶质瘤的分级诊断、鉴别诊断、预后评估等方面的应用研究。     

人工智能在中枢神经疾病影像学中的应用

人工智能（AI）的发展对人类生活的各个领域产生了巨大的影响,尤其是影像医学的天然属性让AI与医学影像的结合走到了医学的前沿。AI在减少医生的主观差错及提高医生的诊断效率方面起到重要作用。就AI在中枢神经系统影像诊疗中的作用及面临的挑战和发展前景进行总结和评述。     

Attitude of Brazilian dentists and dental students regarding the future role of artificial intelligence in oral radiology: a multicenter survey

Objectives:The aim of this study was to assess the attitude of dentists and dental students in Brazil regarding the impact of artificial intelligence (AI) in oral radiology, and to evaluate the effect of an introductory AI lecture on their attitude.Methods:A questionnaire was prepared, comprising statements regarding the future role of AI in oral radiology and dentistry. A lecture of approx. 1 h was prepared, comprising the basic principles of AI and a non-exhaustive overview of AI research in medicine and dentistry. Participants filled in the questionnaire prior to the lecture. After the lecture, the questionnaire was repeated.Results:Throughout 7 sessions at 6 locations, 293 questionnaires were collected. The majority of participants were undergraduate dental students (57%). Prior to the lecture, there was a strong agreement regarding the various future roles and expected impact of AI in oral radiology. Approximately, one-third of participants was concerned about AI. After the lecture, agreement regarding the different roles of AI in oral radiology increased, overall excitement regarding AI increased, and concerns regarding the potential replacement of oral radiologists decreased.Conclusions:A generally positive attitude towards AI was found; an introductory lecture was beneficial towards this attitude and alleviated concerns regarding the effect of AI on the oral radiology profession. Given the unprecedented, ongoing revolution of AI-augmented radiology, it is pivotal to incorporate AI topics in dental training curricula.