以人工智能肠道图像识别模型评估结肠镜检查前肠道准备

目的 观察人工智能肠道图像识别模型用于评估结肠镜检查前肠道准备的价值。方法 回顾性分析190例接受肠道准备评估及结肠镜检查患者，根据评估肠道准备方法将其分为观察组(以人工智能肠道图像识别模型进行判断，n=100)和对照组(仅由患者将末次粪便性状与肠道清洁准备图进行对比而判断，n=90);比较2组肠道清洁度、肠镜检查时间及腺瘤检出率。结果 观察组波士顿肠道准备量表(BBPS)评分及腺瘤检出率高于对照组，而肠镜检查时间短于对照组(P均<0.05)。结论 检查前采用人工智能肠道图像识别模型评估肠道准备情况可提高BBPS评分及腺瘤检出率并缩短肠镜检查时间。     

Artificial intelligence for endoscopy

In recent times, there has been progressive development in artificial intelligence (AI) following the introduction of deep learning in the medical field including gastroenterology and endoscopy. Most of the reported studies were based on retrospective data. Several prospective studies of real-time diagnosis of moving images using the AI system are expected to match the real clinical situation and to aid the endoscopists in the detection and diagnosis of neoplasms without missing any lesion. AI can read a large number of endoscopic images in a few minutes and make a diagnosis; therefore, it is expected to cover the lack of support for the screening esophagogastroduodenoscopy in the health check-up and a large number of capsule images, thereby freeing the endoscopists from this burden. AI can help make the diagnosis during the endoscopic procedure and thereby prevent an unnecessary biopsy for patients taking antithrombotic drugs. AI can also be useful for education and training in endoscopy. Trainees can learn to perform endoscopy and the detection and diagnosis of lesions by the support of AI. In the near future, real-time endoscopic diagnosis using AI is expected to lessen the burden of endoscopists, to enhance the quality level of endoscopists, to overcome the miss of lesions and to make optimal diagnosis.     

人工智能知识图谱和图像分类用于胸部后前位X线片质量控制

目的 观察人工智能(AI)知识图谱和图像分类对胸部后前位X线片(简称胸片)质量控制(QC)的价值。方法 回顾性分析安徽省影像云平台中595家医疗机构共9 236幅胸片,构建包含21个分类标签的QC知识图谱。先由10名技师据此对胸片进行2轮单人QC和1轮多人QC,分别将结果记为A、B、C;再以AI算法进行分类评估,将结果记为D。最后由1名QC专家对C、D进行审核并确定最终QC结果,以之为参考评估上述4种QC效果。结果 AI算法用于胸片QC的曲线下面积(AUC)均≥0.780,平均AUC为0.939。A、B、C、D胸片QC的平均精确率分别为81.15%、85.47%、91.65%、92.21%。结论 AI知识图谱和图像分类技术可有效用于胸部后前位X线片QC。     

Artificial intelligence applications in the intensive care unit

OBJECTIVE: To review the history and current applications of artificial intelligence in the intensive care unit. DATA SOURCES: The MEDLINE database, bibliographies of selected articles, and current texts on the subject. STUDY SELECTION: The studies that were selected for review used artificial intelligence tools for a variety of intensive care applications, including direct patient care and retrospective database analysis. DATA EXTRACTION: All literature relevant to the topic was reviewed. DATA SYNTHESIS: Although some of the earliest artificial intelligence (AI) applications were medically oriented, AI has not been widely accepted in medicine. Despite this, patient demographic, clinical, and billing data are increasingly available in an electronic format and therefore susceptible to analysis by intelligent software. Individual AI tools are specifically suited to different tasks, such as waveform analysis or device control. CONCLUSIONS: The intensive care environment is particularly suited to the implementation of AI tools because of the wealth of available data and the inherent opportunities for increased efficiency in inpatient care. A variety of new AI tools have become available in recent years that can function as intelligent assistants to clinicians, constantly monitoring electronic data streams for important trends, or adjusting the settings of bedside devices. The integration of these tools into the intensive care unit can be expected to reduce costs and improve patient outcomes.     

人工智能骨龄评测系统评估儿童腕骨骨龄

目的 探讨人工智能（AI）系统评测儿童腕骨骨龄的可行性。方法 回顾性分析130幅1~13岁儿童左手骨龄X线片。以3名中高年资放射科医师腕骨骨龄评测结果为参考标准,计算并对比AI系统（简称模型）及3名低年资放射科医师（医师1、2、3,简称医师）与参考标准之间腕骨骨龄和腕骨成熟度分值的均方根误差（RMSE）及平均绝对误差（MAE）;采用组内相关系数（ICC）评价模型、医师与参考标准之间评测骨龄结果的一致性;比较模型与医师间骨龄测评时间。结果 模型与参考标准之间腕骨骨龄的MAE、RMSE与医师1、2与参考标准之间MAE、RMSE差异均有统计学意义（P均<0.05）,与医师3的MAE、RMSE差异无统计学意义（P均>0.05）。模型与参考标准之间腕骨成熟度分值的MAE、RMSE与医师1、参考标准之间MAE、RMSE差异均有统计学意义（P均<0.05）,与医师2、3的MAE、RMSE差异均无统计学意义（P均>0.05）。模型与参考标准之间腕骨骨龄评测结果的ICC=0.997,医师1、2、3与参考标准之间ICC分别为0.994、0.996、0.997。模型对骨龄的测评时间均小于医师（P均<0.001）。结论 AI骨龄评测系统能够准确、快速评估儿童腕骨骨龄。     

Artificial intelligence analysis of paraspinal power spectra

OBJECTIVE: As an aid to discrimination of sufferers with back pain an artificial intelligence neural network was constructed to differentiate paraspinal power spectra. DESIGN: Clinical investigation using surface electromyography. METHOD: The surface electromyogram power spectra from 60 subjects, 33 non-back-pain sufferers and 27 chronic back pain sufferers were used to construct a back propagation neural network that was then tested. Subjects were placed on a test frame in 30 degrees of lumbar forward flexion. An isometric load of two-thirds maximum voluntary contraction was held constant for 30 s whilst surface electromyograms were recorded at the level of the L(4-5). Paraspinal power spectra were calculated and loaded into the input layer of a three-layer back propagation network. The neural network classified the spectra into normal or back pain type. RESULTS: The back propagation neural was shown to have satisfactory convergence with a specificity of 79% and a sensitivity of 80%. CONCLUSIONS: Artificial intelligence neural networks appear to be a useful method of differentiating paraspinal power spectra in back-pain sufferers.     

Artificial intelligence ecosystem for computational psychiatry: Ideas to practice

Computational psychiatry is an emerging field that not only explores the biological basis of mental illness but also considers the diagnoses and identifies the underlying mechanisms. One of the key strengths of computational psychiatry is that it may identify patterns in large datasets that are not easily identifiable. This may help researchers develop more effective treatments and interventions for mental health problems. This paper is a narrative review that reviews the literature and produces an artificial intelligence ecosystem for computational psychiatry. The artificial intelligence ecosystem for computational psychiatry includes data acquisition, preparation, modeling, application, and evaluation. This approach allows researchers to integrate data from a variety of sources, such as brain imaging, genetics, and behavioral experiments, to obtain a more complete understanding of mental health conditions. Through the process of data preprocessing, training, and testing, the data that are required for model building can be prepared. By using machine learning, neural networks, artificial intelligence, and other methods, researchers have been able to develop diagnostic tools that can accurately identify mental health conditions based on a patient’s symptoms and other factors. Despite the continuous development and breakthrough of computational psychiatry, it has not yet influenced routine clinical practice and still faces many challenges, such as data availability and quality, biological risks, equity, and data protection. As we move progress in this field, it is vital to ensure that computational psychiatry remains accessible and inclusive so that all researchers may contribute to this significant and exciting field.     

Artificial neural network technologies to identify biomarkers for therapeutic intervention

High-throughput technologies such as DNA/RNA microarrays, mass spectrometry and protein chips are creating unprecedented opportunities to accelerate towards the understanding of living systems and the identification of target genes and pathways for drug development and therapeutic intervention. However, the increasing volumes of data generated by molecular profiling experiments pose formidable challenges to investigate an overwhelming mass of information and turn it into predictive, deployable markers. Advanced biostatistics and machine learning methods from computer science have been applied to analyze and correlate numerical values of profiling intensities to physiological states. This article reviews the application of artificial neural networks, an information-processing tool, to the identification of sets of diagnostic/prognostic biomarkers from high-throughput profiling data.     

Artificial intelligence in the diagnosis of cirrhosis and portal hypertension

Journal of Medical Ultrasonics - Clinically significant portal hypertension is associated with an increased risk of developing gastroesophageal varices and hepatic decompensation. Hepatic venous...     

Artificial intelligence for COVID-19 and future pandemics: A mini-review

The pandemic of severe acute respiratory syndrome coronavirus 2 has spread very quickly all over the world and has become an unparalleled public health crisis. This unforeseen and exceptional situation has instigated a wave of research to investigate the virus, track its spread, and study the disease it causes. Current methods of diagnosis and monitoring largely rely on polymerase chain reactions and enzyme-linked immunesorbent assay methods. In this hour of crisis, researchers are looking for new technologies to monitor and control such disease outbreaks. Artificial intelligence (AI) is one such technology. Being an evidence-based tool, this technology has the potential to upgrade our disease management strategies and help us to restrict the spread of such diseases. AI can play an effective role in tracking the spread of diseases, screening of the population, identifying patients and developing treatments of diseases. Through this review, we aim to analyze the role of AI in the diagnosis, monitoring and treatment of diseases like coronavirus disease 2019, with most recent updates and assess the prospects of this technology in the management of such diseases.     

Texture analysis of small renal cell carcinomas at MDCT for predicting relevant histologic and protein biomarkers

Abdominal Radiology - To assess CT texture features of small renal cell carcinomas (≤ 4cm) for association with key pathologic features including protein biomarkers. Quantitative CT...     

人工智能在检验医学发展中的重要作用

检验大数据涉及全身各系统并随着疾病的变化而变化,纵横交错,导致目前我们还没有突破检验结果综合分析的瓶颈。借助人工智能,通过检验大数据处理,根据疾病特点对检验数据结果进行全面的综合分析,通过模拟、延伸和扩展将检验数据与疾病的诊断、鉴别诊断、治疗效果评价和预后判断联系起来,产生具有最高水平的智能分析,突破人脑对巨大数据同时...     

Artificial intelligence: An eye cast towards the mental health nursing horizon

There has been an international surge towards online, digital, and telehealth mental health services, further amplified during COVID-19. Implementation and integration of technological innovations, including artificial intelligence (AI), have increased with the intention to improve clinical, governance, and administrative decision-making. Mental health nurses (MHN) should consider the ramifications of these changes and reflect on their engagement with AI. It is time for mental health nurses to demonstrate leadership in the AI mental health discourse and to meaningfully advocate that safety and inclusion of end users' of mental health service interests are prioritized. To date, very little literature exists about this topic, revealing limited engagement by MHNs overall. The aim of this article is to provide an overview of AI in the mental health context and to stimulate discussion about the rapidity and trustworthiness of AI related to the MHN profession. Despite the pace of progress, and personal life experiences with AI, a lack of MHN leadership about AI exists. MHNs have a professional obligation to advocate for access and equity in health service distribution and provision, and this applies to digital and physical domains. Trustworthiness of AI supports access and equity, and for this reason, it is of concern to MHNs. MHN advocacy and leadership are required to ensure that misogynist, racist, discriminatory biases are not favoured in the development of decisional support systems and training sets that strengthens AI algorithms. The absence of MHNs in designing technological innovation is a risk related to the adequacy of the generation of services that are beneficial for vulnerable people such as tailored, precise, and streamlined mental healthcare provision. AI developers are interested to focus on person-like solutions; however, collaborations with MHNs are required to ensure a person-centred approach for future mental healthcare is not overlooked.     

人工智能技术用于超声筛查婴儿发育性髋关节发育不良

目的 构建人工智能(AI)自动识别髋关节超声标准冠状切面及测量相关参数模型,观察其辅助超声筛查婴儿发育性髋关节发育不良(DDH)的价值。方法 [JP2]回顾性分析2 164名婴儿共4 328侧髋关节超声视频,由5名超声科主治医师采用SonoKit标注软件以统一标准于每段视频中选取1幅标准、2幅非标准髋关节冠状切面声像图,并于标准冠状切面图中标注关键解剖结构。经2名超声科主任医师审核,共获得11 100幅声像图(3 665幅标准、7 435幅非标准),以其中8 100幅为训练集(2 665幅标准、5 435幅非标准)、3 000幅为验证集(1 000幅标准、2 000幅非标准)。基于训练集数据构建AI模型,自动识别髋关节超声标准冠状切面,并于其中自动测量α角、β角和股骨头覆盖率(FHC);以验证集验证AI模型识别标准切面的效能。另选取110名健康婴儿的220幅髋关节标准冠状切面声像图,分别由超声科医师手动测量、以AI模型自动测量其α角、β角和FHC,分析测量结果的一致性及相关性。结果 对于验证集髋关节超声标准冠状切面,AI模型与超声科主任医师识别结果的一致性较好(Cohen''s Kappa=0.925);AI模型自动测量与医师手动测量α角、β角及FHC的一致性均较好,组内相关系数分别为0.814、0.730和0.953,均具有强相关性(r=0.826、0.731、0.967)。结论 AI模型能有效自动识别髋关节超声标准冠状切面并自动测量相关参数,可辅助超声筛查婴儿DDH。     

人工智能在医学影像分析中的应用进展

以深度学习（DL）为代表的人工智能（AI）技术已在计算机视觉任务中取得突破性进展。本文从4种常见计算机视觉任务（图像分类、目标检测、物体分割和图像生成）出发,回顾AI技术在医学影像分析中的应用及其发展。     

Artificial intelligence in the GPs office: a retrospective study on diagnostic accuracy

ObjectiveMachine learning (ML) is expected to play an increasing role within primary health care (PHC) in coming years. No peer-reviewed studies exist that evaluate the diagnostic accuracy of ML models compared to general practitioners (GPs). The aim of this study was to evaluate the diagnostic accuracy of an ML classifier on primary headache diagnoses in PHC, compare its performance to GPs, and examine the most impactful signs and symptoms when making a prediction.DesignA retrospective study on diagnostic accuracy, using electronic health records from the database of the Primary Health Care Service of the Capital Area (PHCCA) in Iceland.SettingFifteen primary health care centers of the PHCCA.SubjectsAll patients that consulted a physician, from 1 January 2006 to 30 April 2020, and received one of the selected diagnoses.Main outcome measuresSensitivity, Specificity, Positive Predictive Value, Matthews Correlation Coefficient, Receiver Operating Characteristic (ROC) curve, and Area under the ROC curve (AUROC) score for primary headache diagnoses, as well as Shapley Additive Explanations (SHAP) values of the ML classifier.ResultsThe classifier outperformed the GPs on all metrics except specificity. The SHAP values indicate that the classifier uses the same signs and symptoms (features) as a physician would, when distinguishing between headache diagnoses.ConclusionIn a retrospective comparison, the diagnostic accuracy of the ML classifier for primary headache diagnoses is superior to GPs. According to SHAP values, the ML classifier relies on the same signs and symptoms as a physician when making a diagnostic prediction.

Keypoints

• Little is known about the diagnostic accuracy of machine learning (ML) in the context of primary health care, despite its considerable potential to aid in clinical work. This novel research sheds light on the diagnostic accuracy of ML in a clinical context, as well as the interpretation of its predictions. If the vast potential of ML is to be utilized in primary health care, its performance, safety, and inner workings need to be understood by clinicians.