寻常狼疮16例误诊分析

目的 探讨寻常狼疮的临床特点和病因 ,减少误诊误治。方法 通过对本科门诊近 3个月遇到的 16例在院外误诊为其他皮肤病的寻常狼疮资料进行研究 ,分析其误诊误治的原因。结果 16例分别误诊为结节病、皮肤纤维瘤、鳞状细胞癌、湿疹、深部真菌感染、脂溢性皮炎、盘状红斑狼疮等 ,根据皮损组织病理结果及临床抗痨药物治疗有效 ,最后全部确诊为寻常狼疮。结论 本病皮损的临床表现可有多样 ,自觉症状轻微或全无 ,加之近年又有增多趋势 ,而临床医生又缺乏对本病的足够认识 ,这些是造成误诊的主要原因。

舌轮廓乳头误诊为尖锐湿疣1例

患者女 ,2 4岁。舌根部、外阴发现赘生物 1个月。在当地医院诊断为舌、外阴尖锐湿疣。治疗后舌根部情况无变化 ,后经口腔科医师会诊后诊断舌轮廓乳头 ,无需处理。

掌跖银屑病误诊1例

分析报告 1例被误诊为冻疮和弥漫性掌跖红斑角化症的掌跖银屑病。患者表现为双掌跖红斑鳞屑性皮损 ,反复皲裂 ,行走疼痛 ,手足背散在皮损有薄膜现象和点状出血现象 ,组织病理符合银屑病。

Deep learning-based recognition of stained tongue coating images

Objective To build a dataset encompassing a large number of stained tongue coating images and process it using deep learning to automatically recognize stained tongue coating images.Methods A total of 1001 images of stained tongue coating from healthy students at Hunan University of Chinese Medicine and 1007 images of pathological(non-stained)tongue coat-ing from hospitalized patients at The First Hospital of Hunan University of Chinese Medicine withlungcancer;diabetes;andhypertensionwerecollected.Thetongueimageswererandomi-zed into the training;validation;and testing datasets in a 7:2:1 ratio.A deep learning model was constructed using the ResNet50 for recognizing stained tongue coating in the training and validation datasets.The training period was 90 epochs.The model’s performance was evaluated by its accuracy;loss curve;recall;F1 score;confusion matrix;receiver operating characteristic(ROC)curve;and precision-recall(PR)curve in the tasks of predicting stained tongue coating images in the testing dataset.The accuracy of the deep learning model was compared with that of attending physicians of traditional Chinese medicine(TCM).Results The training results showed that after 90 epochs;the model presented an excellent classification performance.The loss curve and accuracy were stable;showing no signs of overfitting.The model achieved an accuracy;recall;and F1 score of 92%;91%;and 92%;re-spectively.The confusion matrix revealed an accuracy of 92%for the model and 69%for TCM practitioners.The areas under the ROC and PR curves were 0.97 and 0.95;respectively.Conclusion The deep learning model constructed using ResNet50 can effectively recognize stained coating images with greater accuracy than visual inspection of TCM practitioners.This model has the potential to assist doctors in identifying false tongue coating and prevent-ing misdiagnosis.

DEBRIS MONITORING AND ANALYZING SYSTEM (DMAS) AND ITS APPLICATION IN AEROENGINE

By inspecting and analyzing the debris, which is the most direct and important information units in the lubricating oil, we can monitor the machine condition to predict its failure. The debris monitoring and analyzing system (DMAS) is developed from the traditional iron spectrum technology, and has such characteristics as ease for debris separating, forecasting machine failure automatically and accurately in time and so on. The fundamental theory, components and its application in aeroengine health monitoring of DMAS are presented.

Knowledge Discovery in Maintenance Record of FMS Equipment for Diagnosing

To discover the knowledge of fault diagnosis in maintenance record of flexible manufacture system(FMS) equipment. An algorithm (process) was presented, which consists of ① preparatory phase in which some items in maintenance record are selected and decomposed into associated concepts and attributes, and ② discovering and establishing process, in which some possible relationships between the concepts and attributes can be established and knowledge is formulated. The rich diagnosis knowledge in maintenance record was captured through applying the method. An application of the method to the diagnosis system for FMS equipment showed that the approach is correct and effective.

Identification of individuals with non-alcoholic fatty liver disease by the diagnostic criteria for the metabolic syndrome

AIM:To clarify the efficiency of the criterion of metabolic syndrome to detecting non-alcoholic fatty liver disease(NAFLD).METHODS:Authors performed a cross-sectional study involving participants of a medical health checkup program including abdominal ultrasonography.This study involved 11 714 apparently healthy Japanese men and women,18 to 83 years of age.NAFLD was defined by abdominal ultrasonography without an alcohol intake of more than 20 g/d,known liver disease,or current use of medication.The revised criteria of the National Cholesterol Education Program Adult Treatment PanelⅢ were used to characterize the metabolic syndrome.RESULTS:NAFLD was detected in 32.2%(95%CI:31.0%-33.5%)of men(n=1874 of 5811)and in 8.7%(95%CI:8.0%-9.5%)of women(n=514 of 5903).Among obese people,the prevalence of NAFLD was as high as 67.3%(95%CI:64.8%-69.7%)in men and 45.8%(95%CI:41.7%-50.0%)in women.Although NAFLD was thought of as being the liver phenotype of metabolic syndrome,the prevalence of the metabolic syndrome among subjects with NAFLD was low both in men and women.66.8%of men and 70.4%of women with NAFLD were not diagnosed with the metabolic syndrome.48.2%of men with NAFLD and 49.8%of women with NAFLD weren't overweight[body mass index(BMI)≥25 kg/m2].In the same way,68.6%of men with NAFLD and 37.9%of women with NAFLD weren't satisfied with abdominal classification(≥90 cm for men and≥80 cm for women).Next,authors defined it as positive at screening for NAFLD when participants satisfied at least one criterion of metabolic syndrome.The sensitivity of the definition"at least 1 criterion"was as good as 84.8%in men and 86.6%in women.Separating subjects by BMI,the sensitivity was higher in obese men and women than in non-obese men and women(92.3%vs 76.8%in men,96.1%vs 77.0%in women,respectively).CONCLUSION:Authors could determine NAFLD effectively in epidemiological study by modifying the usage of the criteria for metabolic syndrome.

Integrated automatic HAZOP analysis and fault diagnosis based on Petri net

Based on systematically analyzing the procedure of hazard and operability (HAZOP) study, the author introduces a method of modeling fault diagnosis with the Petri net with fuzzy colors, in which the fuzzy information can be represented effectively in the process of analysis. The author proposes the architecture of a knowledge base, which integrates HAZOP analysis and fault diagnosis, and provides the conditions for constructing the knowledge-based expert system. The author also presents a method of knowledge representation for on-line HAZOP analysis and on-line fault diagnosis is presented based on the technology of Petri net with fuzzy colors, which establishes a technological fundamental for integrating the automatic HAZOP analysis and fault diagnosis.

Artificial intelligence meets traditional Chinese medicine: a bridge to opening the magic box of sphygmopalpation for pulse pattern recognition

Artificial intelligence(AI) aims to mimic human cognitive functions and execute intellectual activities like that performed by humans dealing with an uncertain environment. The rapid development of AI technology provides powerful tools to analyze massive amounts of data, facilitating physicians to make better clinical decisions or even replace human judgment in healthcare.Advanced AI technology also creates novel opportunities for exploring the scientific basis of traditional Chinese medicine(TCM) and developing the standardization and digitization of TCM pulse diagnostic methodology. In the present study, we review and discuss the potential application of AI technology in TCM pulse diagnosis. The major contents include the following aspects:(1) a brief introduction of the general concepts and knowledge of TCM pulse diagnosis or palpation,(2) landmark developments in AI technology and the applications of common AI deep learning algorithms in medical practice,(3) the current progress of AI technology in TCM pulse diagnosis,(4) challenges and perspectives of AI technology in TCM pulse diagnosis. In conclusion, the pairing of TCM with modern AI technology will bring novel insights into understanding the scientific principles underlying TCM pulse diagnosis and creating opportunities for the development of AI deep learning technology for the standardization and digitalization of TCM pulse diagnosis.

Static Requirement and Type＇s Complexity in the Diagnostics Phase

The preservation of a historical building, whatever its architectural and/or artistic value, is more successful when undertaken with a deep understanding of the building＇s history, development, materials and construction techniques. The preliminary phase of any restoration intervention must start with data acquisition regarding the characteristics and conditions of the building, including a survey of significant alterations. In a great number of cases, restorations are not respectful of the building＇s static efficiency, so its static requirement is weakened. In fact, a very high percentage of instances in which a restoration effort results in building damage is attributable to such modification＇s indifference to the structural balance of a structure, as consolidated over time. This study focuses on the restoration intervention on two trilobate pillars that separate the central space from the presbytery in the Cathedral of Matera, located in southern Italy. Through the use of sonic tests - carried out despite the complexity of the shape of the building and constructive elements that characterize these pillars -- it was possible to show the effectiveness of the implemented intervention, highlighting critical points and weaknesses. The research aims to show how -- despite the complexity of some structural elements of a building -- in-depth knowledge of a structure＇s structure and history is essential to for the success of restoration interventions, which are respectful of a building＇s type and material peculiarities.

Epidemiological feature,diagnosis and treatment of pancreatic cystadenoma and cystadenocarcinoma:a meta-analysis of 1865 cases

Objective:To study the epidemiological features of pancreatic cystadenoma and cystadenocarcinoma in China during the last 10 years and to analyze the diagnosis and treatment.Methods:Reports on pancreatic cystadenoma and cystadenocarcinoma published from 2000 to 2009 were retrieved from various databases,such as WANFANG data,VIP web and China National Knowledge Infrastructure(CNKI).The epidemiological features of pancreatic cystadenoma and cystadenocarcinoma and its diagnosis and treatment were analyzed.Results:Totally 1 865 patients with pancreatic cystadenoma and cystadenocarcinoma were reported in China during the last 10 years.The male to female ratio was approximately 1:2.1.The accurate ages were reported in 1 536 cases,the average age of them was 50.8 years,whose average age from 40 to 60 years old accounted 75.9% of the patients.65.3% of the cases were located in East China and Central China.Abdominal pain was the main clinical manifestation and was found in 54.9% of the patients.Pancreatoduodenectomy and resection of body and tail of the pancreas were the main procedure for the treatment of pancreatic cystadenoma and cystadenocarcinoma.Conclusion:Pancreatic cystadenoma and cystadenocarcinoma were mainly found in older women in East and Central China.Preoperative diagnosis is difficult.Pancreatoduodenectomy and resection of pancreatic body and tail were the main procedure for the treatment of pancreatic cystadenoma and cystadenocarcinoma.

Gear Fault Recognition and Diagnosis Based on Ant Colony Optimization Algorithm

introduce a new kind of swarm intelligence algorithm, the Ant Colony Optimization （ACO） algorithm. Propose a clustering analysis model based on ACO, apply the model to recognition and diagnosis of operation state for gearbox. Testing four kinds of gears and clustering some characteristic parameters of the gear vibration signal, the conclusion shows that this method can recognize running state with accuracy and all speed. It is a new method for fault recognition and diagnosis.

Perception of pattern identification in traditional medicine:a survey of Korean medical practitioners

OBJECTIVE: To survey assesses the perception of pattern identification(PI) diagnosis of Traditional Medicine(TM)by Korean medical doctors(KMDs).METHODS: A total of 14 485 KMDs affiliated with the Association of Korean Medicine were sent surveys via email, and 1646(11.1%) responded to the questionnaire on their perception of PI diagnosis.RESULTS: Of the 1646 respondents, more than ninety percent(1562, 94.9%) reported that they treated patients using PI.The most critical problem with PI diagnosis was the lack of objective diagnostic indicators(561, 34.1%). Ninety percent had issues diagnosing patients because of different diagnoses between KMDs(1491, 90.5%). The majority of respondents thought herbal medicine was most related to PI(1528, 92.8%). Half of the respondents answered that PI of Ba Gang was the most commonly used PI system. Participants reported that it was most important to study standardisation of PI diagnosis and to develop standardised PI diagnoses using the classification system of the Korean Standard Classification of Diseases. The foremost PI type that physicians thought should be included in standardisation and objectification of PI of TKM was the PIof Ba Gang.CONCLUSION: Our data suggest that we should focus on the standardisation of PI diagnosis and PI of Ba Gang in future research on PI diagnosis inTM.However, we cannot completely discount the possibility that a biased selection of subjects and a low response rate limit the generalisability of the findings.

A new approach to the diagnosis of deficits in processing faces: Potential application in autism research

Deficits in social communication are one of the behavioral signatures of autism spectrum disorder(ASD). Because faces are arguably the most important social stimuli that we encounter in everyday life, investigating the ability of individuals with ASD to process faces is thought to be important for understanding the nature of ASD. However, although a considerable body of evidence suggests that ASD individuals show specific impairments in face processing, a significant number of studies argue otherwise. Through a literature review, we found that this controversy is largely attributable to the different face tests used across different studies. Therefore, a more reliable and valid face test is needed. To this end, we performed a meta-analysis on data gleaned from a variety of face tests conducted on individuals with developmental prosopagnosia(DP) who suffer a selective deficit in face processing. Based on this meta-analysis, we selected an old/new face recognition test that relies on face memory as a standard diagnostic test for measuring specific face processing deficits. This test not only reliably reflects DP individuals' subjective experiences with faces in their daily lives, but also effectively differentiates deficits in face processing from deficits caused by other general problems. In addition, DP individuals' performance in this test predicts their performance in a variety of face tests that examine specific components of face processing(e.g., holistic processing of faces). Finally, this test can be easily administrated and is not overly sensitive to prior knowledge. In summary, this test can be used to evaluate face-processing ability, and it helped to resolve the controversy whether individuals with ASD exhibit face-processing deficits.

A HYBRID PSO-SA OPTIMIZING APPROACH FOR SVM MODELS IN CLASSIFICATION

Support vector machine （SVM） is a widely used tool in the field of image processing and pattern recognition. However, the parameters selection of SVMs is a dilemma in disease identification and clinical diagnosis. This paper proposed an improved parameter optimization method based on traditional particle swarm optimization （PSO） algorithm by changing the fitness function in the traditional evolution process of SVMs. Then, this PSO method was combined with simulated annealing global searching algorithm to avoid local convergence that traditional PSO algorithms usually run into. And this method has achieved better results which reflected in the receiver-operating characteristic curves in medical images classification and has gained considerable identification accuracy in clinical disease detection.