BACKGROUND The use of advanced platelet-rich fibrin(A-PRF)membranes for guided bone and tissue regeneration in through-and-through defects after endodontic surgery was explored in three cases.CASE SUMMARY Herein,three...BACKGROUND The use of advanced platelet-rich fibrin(A-PRF)membranes for guided bone and tissue regeneration in through-and-through defects after endodontic surgery was explored in three cases.CASE SUMMARY Herein,three patients presented to the endodontic clinic suffering from apical periodontitis,associated with large bone resorption and related to previously endodontically treated teeth.Periapical surgery was indicated in these cases and the osteotomy site was covered by A-PRF membrane.Cone-beam computed tomography(CBCT)was used to assess the cases before and after the surgery.CONCLUSION Four months post-surgery,the recall CBCT scan showed complete obliteration of the osteotomy with newly formed bone.A-PRF membrane showed promising results and was an advantageous addition to surgical endodontic treatment.展开更多
Background: Aortico-right ventricular tunnel is an extremely rare congenital defect rarely described in an infant. This diagnosis is likely to be missed due to its rare entity and similar clinical presentations with o...Background: Aortico-right ventricular tunnel is an extremely rare congenital defect rarely described in an infant. This diagnosis is likely to be missed due to its rare entity and similar clinical presentations with other aortico-right ventricular communications like ruptured sinus of valsalva. Methods: We report a case of previously undiagnosed aortico-right ventricular tunnel along with a perimembraneous ventricular septal defect in an 18-year-old female. She had history of exertional dyspnoea, palpitation, history of recurrent lower respiratory tract infection. She was diagnosed as a case of ruptured sinus of valsalva (RSOV) elsewhere. She had “to and fro” murmur, features of congestive cardiac failure. Her echo diagnosis was RSOV. On surgical exploration, after opening the aorta, we found a tunnel like opening in the aorta leading to the roof of RV cavity in between right and non coronary sinuses at the commissural level. Cusps were splayed wide part. Ventricular septal defect was conspicuous from right atrial approach. Post operative CT angio was done. Results: Venricular septal defect was closed from the right atrial approach and aortico-right ventricular tunnel was repaired through aortic and right venricular approach. Postoperative CT angio also confirmed the location and closure of the defects. Postoperative recovery was uneventful. Conclusions: Aortico-right ventricular tunnel in an adult female has not been reported in the literature previously. This rare entity should be considered in the differential diagnosis of a critically ill patient with a “to and fro” murmur, and signs of right heart failure.展开更多
脱空和不密实是隧道衬砌最常见的两种病害。在这两种病害长期作用下会导致隧道出现破裂、渗漏水、钢筋锈蚀,最终造成隧道塌方等问题,严重威胁行车安全。采用探地雷达对隧道进行无损探测是发现这些病害或缺陷的常见方式,但大量雷达数据...脱空和不密实是隧道衬砌最常见的两种病害。在这两种病害长期作用下会导致隧道出现破裂、渗漏水、钢筋锈蚀,最终造成隧道塌方等问题,严重威胁行车安全。采用探地雷达对隧道进行无损探测是发现这些病害或缺陷的常见方式,但大量雷达数据的人工识别存在着工作量大、效率低、强烈依赖人员的专业素养等问题。本文提出一种基于深度学习的隧道衬砌缺陷的自动检测方法——自监督多尺度池化区域卷积神经网络方法(Self-monitoring Multi-scale ROI Align Region Convolutional Neural Network,SMR-RCNN),以提高缺陷识别的效率,并减少主观因素的影响。在雷达探测隧道衬砌的实践中,数据量巨大,但缺陷样本却很少,这对训练神经网络是一个相当大的挑战。为此,设计了一种数据增强的方法来增加缺陷的样本数量,且使用一种自监督对比学习的网络模型来提取雷达数据的特征,然后将其迁移到改进后的Faster-RCNN网络模型中;最后,使用有标签的样本对改进的Faster-RCNN网络进行细调训练。实验结果表明,相较于传统的Faster-RCNN方法,本文提出的算法增强了神经网络对脱空和不密实两类缺陷的自动识别能力,在检测精度上得到了显著提高,mAP值提升了12%。展开更多
基金Supported by the Princess Nourah Bint Abdulrahman University Researchers Supporting Project,No.PNURSP2023R363.
文摘BACKGROUND The use of advanced platelet-rich fibrin(A-PRF)membranes for guided bone and tissue regeneration in through-and-through defects after endodontic surgery was explored in three cases.CASE SUMMARY Herein,three patients presented to the endodontic clinic suffering from apical periodontitis,associated with large bone resorption and related to previously endodontically treated teeth.Periapical surgery was indicated in these cases and the osteotomy site was covered by A-PRF membrane.Cone-beam computed tomography(CBCT)was used to assess the cases before and after the surgery.CONCLUSION Four months post-surgery,the recall CBCT scan showed complete obliteration of the osteotomy with newly formed bone.A-PRF membrane showed promising results and was an advantageous addition to surgical endodontic treatment.
文摘Background: Aortico-right ventricular tunnel is an extremely rare congenital defect rarely described in an infant. This diagnosis is likely to be missed due to its rare entity and similar clinical presentations with other aortico-right ventricular communications like ruptured sinus of valsalva. Methods: We report a case of previously undiagnosed aortico-right ventricular tunnel along with a perimembraneous ventricular septal defect in an 18-year-old female. She had history of exertional dyspnoea, palpitation, history of recurrent lower respiratory tract infection. She was diagnosed as a case of ruptured sinus of valsalva (RSOV) elsewhere. She had “to and fro” murmur, features of congestive cardiac failure. Her echo diagnosis was RSOV. On surgical exploration, after opening the aorta, we found a tunnel like opening in the aorta leading to the roof of RV cavity in between right and non coronary sinuses at the commissural level. Cusps were splayed wide part. Ventricular septal defect was conspicuous from right atrial approach. Post operative CT angio was done. Results: Venricular septal defect was closed from the right atrial approach and aortico-right ventricular tunnel was repaired through aortic and right venricular approach. Postoperative CT angio also confirmed the location and closure of the defects. Postoperative recovery was uneventful. Conclusions: Aortico-right ventricular tunnel in an adult female has not been reported in the literature previously. This rare entity should be considered in the differential diagnosis of a critically ill patient with a “to and fro” murmur, and signs of right heart failure.
文摘脱空和不密实是隧道衬砌最常见的两种病害。在这两种病害长期作用下会导致隧道出现破裂、渗漏水、钢筋锈蚀,最终造成隧道塌方等问题,严重威胁行车安全。采用探地雷达对隧道进行无损探测是发现这些病害或缺陷的常见方式,但大量雷达数据的人工识别存在着工作量大、效率低、强烈依赖人员的专业素养等问题。本文提出一种基于深度学习的隧道衬砌缺陷的自动检测方法——自监督多尺度池化区域卷积神经网络方法(Self-monitoring Multi-scale ROI Align Region Convolutional Neural Network,SMR-RCNN),以提高缺陷识别的效率,并减少主观因素的影响。在雷达探测隧道衬砌的实践中,数据量巨大,但缺陷样本却很少,这对训练神经网络是一个相当大的挑战。为此,设计了一种数据增强的方法来增加缺陷的样本数量,且使用一种自监督对比学习的网络模型来提取雷达数据的特征,然后将其迁移到改进后的Faster-RCNN网络模型中;最后,使用有标签的样本对改进的Faster-RCNN网络进行细调训练。实验结果表明,相较于传统的Faster-RCNN方法,本文提出的算法增强了神经网络对脱空和不密实两类缺陷的自动识别能力,在检测精度上得到了显著提高,mAP值提升了12%。
