Federated learning has recently attracted significant attention as a cutting-edge technology that enables Artificial Intelligence(AI)algorithms to utilize global learning across the data of numerous individuals while ...Federated learning has recently attracted significant attention as a cutting-edge technology that enables Artificial Intelligence(AI)algorithms to utilize global learning across the data of numerous individuals while safeguarding user data privacy.Recent advanced healthcare technologies have enabled the early diagnosis of various cognitive ailments like Parkinson’s.Adequate user data is frequently used to train machine learning models for healthcare systems to track the health status of patients.The healthcare industry faces two significant challenges:security and privacy issues and the personalization of cloud-trained AI models.This paper proposes a Deep Neural Network(DNN)based approach embedded in a federated learning framework to detect and diagnose brain disorders.We extracted the data from the database of Kay Elemetrics voice disordered and divided the data into two windows to create training models for two clients,each with different data.To lessen the over-fitting aspect,every client reviewed the outcomes in three rounds.The proposed model identifies brain disorders without jeopardizing privacy and security.The results reveal that the global model achieves an accuracy of 82.82%for detecting brain disorders while preserving privacy.展开更多
目的探讨联合甲状腺结节超声恶性危险分层中国指南(Chinese-Thyroid Imaging Reporting and Data System,C-TIRADS)构建的超声辅助诊断模型对甲状腺结节良恶性筛查的应用价值。方法回顾性分析2022年4月—2023年4月在江门市五邑中医院进...目的探讨联合甲状腺结节超声恶性危险分层中国指南(Chinese-Thyroid Imaging Reporting and Data System,C-TIRADS)构建的超声辅助诊断模型对甲状腺结节良恶性筛查的应用价值。方法回顾性分析2022年4月—2023年4月在江门市五邑中医院进行超声检查并明确病理结果的甲状腺结节患者(共136例患者,180个病灶),依据C-TIRADS指南对甲状腺结节进行分类评估,然后使用AI辅助诊断联合C-TIRADS再次进行分类评估,以病理结果为金标准,绘制C-TIRADS诊断与AI联合C-TIRADS诊断的ROC曲线,比较两种诊断方法的AUC及敏感度、特异度、准确度等指标,分析两组指标差异。绘制校准曲线和DCA曲线进行验证对比,评价其校准能力和临床效用。结果180个甲状腺结节病灶经手术病理证实良性87个,恶性93个。C-TIRADS诊断与AI联合C-TIRADS诊断对甲状腺结节良恶性诊断的AUC分别为0.714、0.800,AI联合C-TIRADS诊断明显高于C-TIRADS诊断,差异有统计学意义(P<0.001)。两种诊断方法均有良好的校准能力和临床效用,AI联合C-TIRADS诊断较C-TIRADS诊断更优。结论联合C-TIRADS的AI辅助诊断模型在甲状腺结节良恶性的诊断中具有良好的诊断效能、校准能力及临床效用,能有效减少甲状腺结节的过度诊疗,对临床决策有一定参考意义。展开更多
Objective To investigate th e value of proton magnetic resonance spectroscopy ( 1H-MRS) on diagnosis a nd differential diagnosis of the intracranial diseases by the MRS results of 52 patients. Methods 12 patients ...Objective To investigate th e value of proton magnetic resonance spectroscopy ( 1H-MRS) on diagnosis a nd differential diagnosis of the intracranial diseases by the MRS results of 52 patients. Methods 12 patients with benign glioma, 16 patients with malignant glioma, 10 patients with meningioma, 8 patients with virus encephalitis, and 6 patients with cerebral infarction underwent MRS in th e lesion region. We measured the area within the spectra of N-acetyl-aspartate (NAA), creatine/phosphocreatine (Cr), choline compounds (Cho), and lactate (Lac ). Results The spectra of meningiomas were characterized by abs ence of NAA. The spectra of gliomas were characterized by the decrease of NAA an d Cr, but the increase of Cho. The ratio of Cho to Cr was 2.25±1.21 in benign g liomas, while the ratio of Cho to Cr was 4.65±2.21 in malignant gliomas. The sp ectra of virus encephalitis appeared the decrease of NAA and the normality of Cr , with the 1.25±0.21 ratio of Cho/Cr. The apparent Lac wave could be seen in al l cerebral infarctions. Conclusion The value of 1H-MRS plays a significant role in the diagnosis and differential diagnosis of gliomas, mening iomas, virus encephalitis, and cerebral infarctions.展开更多
The work condition of nuclear power plant (NPP) is very bad, which makes it has faults easily. In order to diagnose the faults real time, the fusion diagnosis system is built. The data fusion fault diagnosis system ad...The work condition of nuclear power plant (NPP) is very bad, which makes it has faults easily. In order to diagnose the faults real time, the fusion diagnosis system is built. The data fusion fault diagnosis system adopts data fusion method and divides the fault diagnosis into three levels, which are data fusion level, feature level and decision level. The feature level uses three parallel neural networks whose structures are the same. The purpose of using neural networks is mainly to get basic probability assignment (BPA) of D-S evidence theory, and the neural networks in feature level are used for local diagnosis. D-S evidence theory is adopted to integrate the local diagnosis results in decision level. The reactor coolant system is the study object and we choose 2# steam generator U-tubes break of the reactor coolant system as a diagnostic example. The experiments prove that the fusion diagnosis system can satisfy the fault diagnosis requirement of complicated system, and verify that the fusion fault diagnosis system can realize the fault diagnosis of NPP on line timely.展开更多
Raman spectroscopy is a spectroscopic technique based on the inelastic scattering of monochromatic light that represents the molecular composition of the interrogated volume to provide a direct molecular fingerprint. ...Raman spectroscopy is a spectroscopic technique based on the inelastic scattering of monochromatic light that represents the molecular composition of the interrogated volume to provide a direct molecular fingerprint. Several investigations have revealed that confocal Raman spectroscopy can differentiate non-dysplastic Barrett's esophagus from esophageal high-grade dysplasia and adenocarcinoma with high sensitivity and specificity. An automated on-line Raman spectral diagnostic system has made it possible to use Raman spectroscopy to guide accurate target biopsy instead of multiple random forceps-biopsies,this novel system is expected to improve in vivo precancerous diagnosis and tissue characterization of Barrett's esophagus.展开更多
基金supported by the Deanship of Scientific Research at Prince Sattam bin Aziz University under the Research Project (PSAU/2023/01/22425).
文摘Federated learning has recently attracted significant attention as a cutting-edge technology that enables Artificial Intelligence(AI)algorithms to utilize global learning across the data of numerous individuals while safeguarding user data privacy.Recent advanced healthcare technologies have enabled the early diagnosis of various cognitive ailments like Parkinson’s.Adequate user data is frequently used to train machine learning models for healthcare systems to track the health status of patients.The healthcare industry faces two significant challenges:security and privacy issues and the personalization of cloud-trained AI models.This paper proposes a Deep Neural Network(DNN)based approach embedded in a federated learning framework to detect and diagnose brain disorders.We extracted the data from the database of Kay Elemetrics voice disordered and divided the data into two windows to create training models for two clients,each with different data.To lessen the over-fitting aspect,every client reviewed the outcomes in three rounds.The proposed model identifies brain disorders without jeopardizing privacy and security.The results reveal that the global model achieves an accuracy of 82.82%for detecting brain disorders while preserving privacy.
文摘目的探讨联合甲状腺结节超声恶性危险分层中国指南(Chinese-Thyroid Imaging Reporting and Data System,C-TIRADS)构建的超声辅助诊断模型对甲状腺结节良恶性筛查的应用价值。方法回顾性分析2022年4月—2023年4月在江门市五邑中医院进行超声检查并明确病理结果的甲状腺结节患者(共136例患者,180个病灶),依据C-TIRADS指南对甲状腺结节进行分类评估,然后使用AI辅助诊断联合C-TIRADS再次进行分类评估,以病理结果为金标准,绘制C-TIRADS诊断与AI联合C-TIRADS诊断的ROC曲线,比较两种诊断方法的AUC及敏感度、特异度、准确度等指标,分析两组指标差异。绘制校准曲线和DCA曲线进行验证对比,评价其校准能力和临床效用。结果180个甲状腺结节病灶经手术病理证实良性87个,恶性93个。C-TIRADS诊断与AI联合C-TIRADS诊断对甲状腺结节良恶性诊断的AUC分别为0.714、0.800,AI联合C-TIRADS诊断明显高于C-TIRADS诊断,差异有统计学意义(P<0.001)。两种诊断方法均有良好的校准能力和临床效用,AI联合C-TIRADS诊断较C-TIRADS诊断更优。结论联合C-TIRADS的AI辅助诊断模型在甲状腺结节良恶性的诊断中具有良好的诊断效能、校准能力及临床效用,能有效减少甲状腺结节的过度诊疗,对临床决策有一定参考意义。
文摘Objective To investigate th e value of proton magnetic resonance spectroscopy ( 1H-MRS) on diagnosis a nd differential diagnosis of the intracranial diseases by the MRS results of 52 patients. Methods 12 patients with benign glioma, 16 patients with malignant glioma, 10 patients with meningioma, 8 patients with virus encephalitis, and 6 patients with cerebral infarction underwent MRS in th e lesion region. We measured the area within the spectra of N-acetyl-aspartate (NAA), creatine/phosphocreatine (Cr), choline compounds (Cho), and lactate (Lac ). Results The spectra of meningiomas were characterized by abs ence of NAA. The spectra of gliomas were characterized by the decrease of NAA an d Cr, but the increase of Cho. The ratio of Cho to Cr was 2.25±1.21 in benign g liomas, while the ratio of Cho to Cr was 4.65±2.21 in malignant gliomas. The sp ectra of virus encephalitis appeared the decrease of NAA and the normality of Cr , with the 1.25±0.21 ratio of Cho/Cr. The apparent Lac wave could be seen in al l cerebral infarctions. Conclusion The value of 1H-MRS plays a significant role in the diagnosis and differential diagnosis of gliomas, mening iomas, virus encephalitis, and cerebral infarctions.
文摘The work condition of nuclear power plant (NPP) is very bad, which makes it has faults easily. In order to diagnose the faults real time, the fusion diagnosis system is built. The data fusion fault diagnosis system adopts data fusion method and divides the fault diagnosis into three levels, which are data fusion level, feature level and decision level. The feature level uses three parallel neural networks whose structures are the same. The purpose of using neural networks is mainly to get basic probability assignment (BPA) of D-S evidence theory, and the neural networks in feature level are used for local diagnosis. D-S evidence theory is adopted to integrate the local diagnosis results in decision level. The reactor coolant system is the study object and we choose 2# steam generator U-tubes break of the reactor coolant system as a diagnostic example. The experiments prove that the fusion diagnosis system can satisfy the fault diagnosis requirement of complicated system, and verify that the fusion fault diagnosis system can realize the fault diagnosis of NPP on line timely.
文摘Raman spectroscopy is a spectroscopic technique based on the inelastic scattering of monochromatic light that represents the molecular composition of the interrogated volume to provide a direct molecular fingerprint. Several investigations have revealed that confocal Raman spectroscopy can differentiate non-dysplastic Barrett's esophagus from esophageal high-grade dysplasia and adenocarcinoma with high sensitivity and specificity. An automated on-line Raman spectral diagnostic system has made it possible to use Raman spectroscopy to guide accurate target biopsy instead of multiple random forceps-biopsies,this novel system is expected to improve in vivo precancerous diagnosis and tissue characterization of Barrett's esophagus.
