Towards updated understanding of brain metastasis

Brain metastasis (BM) is a common complication in cancer patients with advanced disease and attributes to treatment failure and final mortality. Currently there are several therapeutic options available; however these are only suitable for limited subpopulation: surgical resection or radiosurgery for cases with a limited number of lesions, targeted therapies for approximately 18% of patients, and immune checkpoint inhibitors with a response rate of 20-30%. Thus, there is a pressing need for development of novel diagnostic and therapeutic options. This overview article aims to provide research advances in disease model, targeted therapy, blood brain barrier (BBB) opening strategies, imaging and its incorporation with artificial intelligence, external radiotherapy, and internal targeted radionuclide theragnostics. Finally, a distinct type of BM, leptomeningeal metastasis is also covered.     

PROTEIN AI Advisor: A Knowledge-Based Recommendation Framework Using Expert-Validated Meals for Healthy Diets

AI-based software applications for personalized nutrition have recently gained increasing attention to help users follow a healthy lifestyle. In this paper, we present a knowledge-based recommendation framework that exploits an explicit dataset of expert-validated meals to offer highly accurate diet plans spanning across ten user groups of both healthy subjects and participants with health conditions. The proposed advisor is built on a novel architecture that includes (a) a qualitative layer for verifying ingredient appropriateness, and (b) a quantitative layer for synthesizing meal plans. The first layer is implemented as an expert system for fuzzy inference relying on an ontology of rules acquired by experts in Nutrition, while the second layer as an optimization method for generating daily meal plans based on target nutrient values and ranges. The system’s effectiveness is evaluated through extensive experiments for establishing meal and meal plan appropriateness, meal variety, as well as system capacity for recommending meal plans. Evaluations involved synthetic data, including the generation of 3000 virtual user profiles and their weekly meal plans. Results reveal a high precision and recall for recommending appropriate ingredients in most user categories, while the meal plan generator achieved a total recommendation accuracy of 92% for all nutrient recommendations.     

Application of artificial intelligence in diagnosing COVID-19 disease symptoms on chest X-rays: A systematic review

This systematic review focuses on using artificial intelligence (AI) to detect COVID-19 infection with the help of X-ray images.Methodology: In January 2022, the authors searched PubMed, Embase and Scopus using specific medical subject headings terms and filters. All articles were independently reviewed by two reviewers. All conflicts resulting from a misunderstanding were resolved by a third independent researcher. After assessing abstracts and article usefulness, eliminating repetitions and applying inclusion and exclusion criteria, six studies were found to be qualified for this study.Results: The findings from individual studies differed due to the various approaches of the authors. Sensitivity was 72.59%-100%, specificity was 79%-99.9%, precision was 74.74%-98.7%, accuracy was 76.18%-99.81%, and the area under the curve was 95.24%-97.7%.Conclusion: AI computational models used to assess chest X-rays in the process of diagnosing COVID-19 should achieve sufficiently high sensitivity and specificity. Their results and performance should be repeatable to make them dependable for clinicians. Moreover, these additional diagnostic tools should be more affordable and faster than the currently available procedures. The performance and calculations of AI-based systems should take clinical data into account.     

基于医院临床数据中心的可疑呼吸道传染病发现与预测模型构建

目的 通过构建基于临床数据中心的可疑呼吸道传染病发现与预测模型, 实现对可疑传染病的发现与预测。 方法 选取某三甲医院的临床数据, 基于历史传染病数据进行病历结构化建模, 构建呼吸道传染病知识图谱, 利用XGboost算法和知识图谱推理技术形成发现与预测合并决策模型, 并使用医院历史数据做交叉验证, 得到准确度较高的模型。 结果 发现与预测模型的平均查准率为92.55%, 查全率为91.49%, 综合F1值为92.01%, 均优于单独的知识图谱模型或XGboost模型, 将模型与医院的电子病历系统和临床辅助决策系统进行集成, 应用于对真实临床病例的预测。 结论 该方法能够很好地针对新发可疑呼吸道传染病进行预测, 辅助医院及时启动传染病应急预案, 减少传染病发生早期时医务人员的感染概率。     

Use of Artificial Intelligence for Reducing Unnecessary Recalls at Screening Mammography: A Simulation Study

ObjectiveTo conduct a simulation study to determine whether artificial intelligence (AI)-aided mammography reading can reduce unnecessary recalls while maintaining cancer detection ability in women recalled after mammography screening.Materials and MethodsA retrospective reader study was performed by screening mammographies of 793 women (mean age ± standard deviation, 50 ± 9 years) recalled to obtain supplemental mammographic views regarding screening mammography-detected abnormalities between January 2016 and December 2019 at two screening centers. Initial screening mammography examinations were interpreted by three dedicated breast radiologists sequentially, case by case, with and without AI aid, in a single session. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and recall rate for breast cancer diagnosis were obtained and compared between the two reading modes.ResultsFifty-four mammograms with cancer (35 invasive cancers and 19 ductal carcinomas in situ) and 739 mammograms with benign or negative findings were included. The reader-averaged AUC improved after AI aid, from 0.79 (95% confidence interval [CI], 0.74–0.85) to 0.89 (95% CI, 0.85–0.94) (p < 0.001). The reader-averaged specificities before and after AI aid were 41.9% (95% CI, 39.3%–44.5%) and 53.9% (95% CI, 50.9%–56.9%), respectively (p < 0.001). The reader-averaged sensitivity was not statistically different between AI-unaided and AI-aided readings: 89.5% (95% CI, 83.1%–95.9%) vs. 92.6% (95% CI, 86.2%–99.0%) (p = 0.053), although the sensitivities of the least experienced radiologists before and after AI aid were 79.6% (43 of 54 [95% CI, 66.5%–89.4%]) and 90.7% (49 of 54 [95% CI, 79.7%–96.9%]), respectively (p = 0.031). With AI aid, the reader-averaged recall rate decreased by from 60.4% (95% CI, 57.8%–62.9%) to 49.5% (95% CI, 46.5%–52.4%) (p < 0.001).ConclusionAI-aided reading reduced the number of recalls and improved the diagnostic performance in our simulation using women initially recalled for supplemental mammographic views after mammography screening.     

Evaluating the Effect of a COVID-19 Predictive Model to Facilitate Discharge: A Randomized Controlled Trial

Background  We previously developed and validated a predictive model to help clinicians identify hospitalized adults with coronavirus disease 2019 (COVID-19) who may be ready for discharge given their low risk of adverse events. Whether this algorithm can prompt more timely discharge for stable patients in practice is unknown. Objectives  The aim of the study is to estimate the effect of displaying risk scores on length of stay (LOS). Methods  We integrated model output into the electronic health record (EHR) at four hospitals in one health system by displaying a green/orange/red score indicating low/moderate/high-risk in a patient list column and a larger COVID-19 summary report visible for each patient. Display of the score was pseudo-randomized 1:1 into intervention and control arms using a patient identifier passed to the model execution code. Intervention effect was assessed by comparing LOS between intervention and control groups. Adverse safety outcomes of death, hospice, and re-presentation were tested separately and as a composite indicator. We tracked adoption and sustained use through daily counts of score displays. Results  Enrolling 1,010 patients from May 15, 2020 to December 7, 2020, the trial found no detectable difference in LOS. The intervention had no impact on safety indicators of death, hospice or re-presentation after discharge. The scores were displayed consistently throughout the study period but the study lacks a causally linked process measure of provider actions based on the score. Secondary analysis revealed complex dynamics in LOS temporally, by primary symptom, and hospital location. Conclusion  An AI-based COVID-19 risk score displayed passively to clinicians during routine care of hospitalized adults with COVID-19 was safe but had no detectable impact on LOS. Health technology challenges such as insufficient adoption, nonuniform use, and provider trust compounded with temporal factors of the COVID-19 pandemic may have contributed to the null result. Trial registration  ClinicalTrials.gov identifier: {"type":"clinical-trial","attrs":{"text":"NCT04570488","term_id":"NCT04570488"}}NCT04570488.     

免费医学定向生专业承诺对职业成熟度的影响——情绪智力的中介作用

目的 探讨免费医学定向生专业承诺对职业成熟度的影响机制,为提升免费医学定向生的职业成熟度提供理论依据。方法 采用大学生专业承诺量表、情绪量表和大学生职业成熟度量表,对370名免费医学定向生进行调查。结果 免费医学定向生专业承诺与情绪智力、职业成熟度两两变量之间均呈正相关(P<0.05),情绪智力在免费医学定向生专业承诺与职业成熟度间起部分中介作用。结论 提高并践行免费医学定向生的专业承诺,注重其情绪智力的培养与教育,有利于提升免费医学定向生的职业成熟度。     

资源需求型智慧教育模式在康复评定学教学中的应用

在信息技术不断变革创新的时代,智慧教育模式在逐渐取代传统的教学模式,学习者"学习方式"正在发生深刻的变革,对学习资源的需求也在改变,人们需要学习资源多样化、微型化、切需化,以适应新的学习文化和学习方式.在我校康复评定学课程教学中,我们引入智慧教育模式,开展康复评定学课程"资源需求型"研究,以期探索符合当前信息时代的高等...     

基于韦氏成人智力量表探索肝郁脾虚抑郁症患者认知功能特征

目的初步探索肝郁脾虚抑郁症患者认知功能特征。方法制定《肝郁脾虚中医证候观察表》,搜集抑郁症肝郁脾虚组患者30例,抑郁症非肝郁脾虚组患者29例,健康对照组23例,进行人口学资料（年龄、性别、学历）、汉密顿抑郁量表（HAMD）、汉密顿焦虑量表（HAMA）、韦氏成人智力量表的观察（WAIS-RC）。排除文化程度、情绪障碍轻重对认知的影响,分析WAIS-RC各因子,初步探索肝郁脾虚抑郁症患者认知功能特征。抑郁症两组情绪量表总分比较经t检验;WAIS—RC各因子经方差分析,如P〈0．05两两之间再经SNK检验。结果（1）抑郁症肝郁脾虚和非肝郁脾虚组在HAMD、HAMA总分上无明显统计学差异（P〉0．05）。（2）在相似性、数字广度、图画填充、物体拼凑、言语量表、操作量表、全量表因子上P〈0．05,提示三组存在明显统计学差异,两两之间再进行SNK检验。经检验在相似性、数字广度、言语量表、操作量表、全量表因子上抑郁症肝郁脾虚组和非肝郁脾虚组与健康对照组存在明显统计学差异（P〈0．05）,抑郁症两组间无差异;在物体拼凑因子上抑郁症肝郁脾虚组均值均小于非肝郁脾虚组及健康对照组,与两组间均存在明显统计学差异（P〈0．05）,非肝郁脾虚组及健康对照组无差异;在图画填充因子上抑郁症肝郁脾虚组均值大于健康对照组,两组存在明显统计学差异（P〈0．05）,抑郁症非肝郁脾虚组与其它两组无明显统计学差异。在知识、领悟、算术、词汇、数字符号、木块图案、图片排列因子上P〉0．05,提示三组在以上因子上无明显统计学差异。结论抑郁症患者存在不同程度语言智商、操作智商的下降,主要表现为注意力下降、思维不够灵活、短时记忆力下降、信息加工能力差、归纳推理能力下降等,长时记忆力不受影响。且肝郁脾虚型较非肝郁脾虚型抑郁症患者信息加工能力更差,思维和推理能力更迟缓。     

浅谈人工智能对中医诊断的影响＊

中医药是中华民族的瑰宝，随着人工智能（Artificial Intelligence，AI）的迅猛发展，中医药与AI的结合将可以为中医药的传承和现代化提供助力。本研究将从搭建AI中医专家系统入手，阐述AI对提高中医诊断的作用，物联网搭建AI中医药服务，存在的问题和对策，以及中医药人工智能化的前景，以图为中医药的发展提供新的思路和方法。