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目的评估数字肺音人工智能分析与临床医师判断的一致性。方法本研究为诊断性实验。采用非随机抽样方法应用2737条选自《国家远程医疗与互联网医学中心数字听诊平台》的真实的临床数字肺音数据,应用Luntech?数字听诊人工智能分析结果判断,根据肺音的粗糙程度、是否有啰音、啰音的强度、是否有干啰音和是否有湿啰音5个方面进行分类整理后建立人工智能分析结果数据库。同时,制定统一分类定义,临床医师对数字肺音进行以上5个方面判断,对比分析数字听诊人工智能和医师听诊的肺音分析结果的一致性。应用描述性方法对肺音图变化特征进行整体描述;以医师判断结果为"金标准",应用混淆矩阵计算分类准确度、召回率、虚警率、精确度、kappa值评价两者的一致性。结果Luntech?数字听诊人工智能分析对于是否为异常肺音、有无呼吸音粗糙、有无啰音、有无干啰音和有无湿啰音的判断准确度分别为98.39%、95.14%、96.60%、97.84%和96.97%,召回率分别为96.60%、88.34%、91.65%、92.70%和86.68%,虚警率分别为3.48%、2.43%、1.03%、0.92%和0.63%,精确度分别为97.00%、92.86%、97.71%、97.08%和96.98%;并且一致性良好(kappa值分别为0.931、0.873、0.921、0.941和0.898);对于啰音强度的判断结果具有较好的一致性(湿啰音kappa值=0.790,干啰音kappa值=0.889)。肺音图具有明显形态特征,人工智能肺音分析指数及肺音图可敏感反应肺音性质的变化。结论数字肺音人工智能分析及肺音图与临床医师判断有较好一致性。  相似文献   
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《Dental materials》2022,38(12):2062-2072
ObjectivesTo investigate the effect of a protective coating on the surface characteristics, flexural properties, cytotoxicity, and microbial adhesion of vat-photopolymerization additive-manufacturing denture base polymers.MethodsThe specimens were additively manufactured using digital light processing (DLP). Specimen surfaces were coated with the same printed resin, and mechanical polishing was used for comparison. Surface topography, arithmetical mean height (Sa), and water contact angle values were measured. Furthermore, flexural strength (FS)/modulus and fractography were evaluated. Also, cytotoxicity was evaluated by an extract test. Finally, an adhesion test was used to investigate the adhesion of mixed oral bacteria to the specimens.ResultsThe Sa values in the polished (0.26 ± 0.08 µm) and coated (0.38 ± 0.14 µm) groups were significantly lower than in the untreated (2.21 ± 0.42 µm) and control (2.01 ± 0.37 µm) groups. The coating treatment resulted in a higher FS compared to the untreated surface (p = 0.0002). After the coating treatment, no significant differences were found in relative cell viability between the groups (p > 0.05). The quantitative results showed significantly higher bacterial adhesion in the untreated group than in the polished (p = 0.0047) and coated (p < 0.0001) groups.SignificanceThe surface characteristics and flexural properties were optimized by the protective coating. Also, the protective coating did not adversely affect cytocompatibility. Moreover, the coating treatment could effectively decrease oral bacteria adhering to the surfaces. Therefore, the protective coating treatment can be a less time-consuming alternative to mechanical polishing as a post-processing procedure for the digital denture.  相似文献   
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BackgroundPeople living with multiple sclerosis (MS) experience impairments in gait and mobility, that are not fully captured with manually timed walking tests or rating scales administered during periodic clinical visits. We have developed a smartphone-based assessment of ambulation performance, the 5 U-Turn Test (5UTT), a quantitative self-administered test of U-turn ability while walking, for people with MS (PwMS).Research questionWhat is the test-retest reliability and concurrent validity of U-turn speed, an unsupervised self-assessment of gait and balance impairment, measured using a body-worn smartphone during the 5UTT?Methods76 PwMS and 25 healthy controls (HCs) participated in a cross-sectional non-randomised interventional feasibility study. The 5UTT was self-administered daily and the median U-turn speed, measured during a 14-day session, was compared against existing validated in-clinic measures of MS-related disability.ResultsU-turn speed, measured during a 14-day session from the 5UTT, demonstrated good-to-excellent test-retest reliability in PwMS alone and combined with HCs (intraclass correlation coefficient [ICC] = 0.87 [95 % CI: 0.80–0.92]) and moderate-to-excellent reliability in HCs alone (ICC = 0.88 [95 % CI: 0.69–0.96]). U-turn speed was significantly correlated with in-clinic measures of walking speed, physical fatigue, ambulation impairment, overall MS-related disability and patients’ self-perception of quality of life, at baseline, Week 12 and Week 24. The minimal detectable change of the U-turn speed from the 5UTT was low (19.42 %) in PwMS and indicates a good precision of this measurement tool when compared with conventional in-clinic measures of walking performance.SignificanceThe frequent self-assessment of turn speed, as an outcome measure from a smartphone-based U-turn test, may represent an ecologically valid digital solution to remotely and reliably monitor gait and balance impairment in a home environment during MS clinical trials and practice.  相似文献   
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AimSkin tears are traumatic wounds characterised by separation of the skin layers. Severity evaluation is important in the management of skin tears. To support the assessment and management of skin tears, this study aimed to develop an algorithm to estimate a category of the Skin Tear Audit Research classification system (STAR classification) using digital images via machine learning. This was achieved by introducing shape features representing complicated shape of the skin tears.MethodsA skin tear image was separated into small segments, and features of each segment were estimated. The segments were then classified into different classes by machine learning algorithms, namely support vector machine and random forest. Their performance in classifying wound segments and STAR categories was evaluated with 31 images using the leave-one-out cross validation.ResultsSupport vector machine showed an accuracy of 74% and 69% in classifying wound segments and STAR categories, respectively. The corresponding accuracy using random forest were 71% and 63%.ConclusionMachine learning algorithms revealed capable of classifying categories of skin tears. This could offer the potential to aid nurses in their management of skin tears, even if they are not specialised in wound care.  相似文献   
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