In Frontiers in neurology

OBJECTIVE : Microelectrode recording (MER) guided subthalamic nucleus deep brain stimulation (STN-DBS) under local anesthesia (LA) is widely applied in the management of advanced Parkinson's disease (PD). Whereas, awake DBS under LA is painful and burdensome for PD patients. We analyzed the influence of general anesthesia (GA) on intraoperative MER, to assess the feasibility and effectiveness of GA in MER guided STN-DBS.

METHODS : Retrospective analysis was performed on the PD patients, who underwent bilateral MER guided STN-DBS in Wuhan Union Hospital from July 2019 to December 2021. The patients were assigned to LA or GA group according to the anesthetic methods implemented. Multidimensional parameters, including MER signals, electrode implantation accuracy, clinical outcome and adverse events, were analyzed.

RESULTS : A total of 40 PD patients were enrolled in this study, including 18 in LA group and 22 in GA group. There were no statistically significant differences in patient demographics and baseline characteristics between two groups. Although, the parameters of MER signal, including frequency, inter-spike interval (ISI) and amplitude, were obviously interfered under GA, the waveforms of MER signals were recognizable and shared similar characteristics with LA group. Both LA and GA could achieve effective electrode implantation accuracy and clinical outcome. They also shared similar adverse events postoperatively.

CONCLUSION : GA is viable and comparable to LA in MER guided STN-DBS for PD, regarding electrode implantation accuracy, clinical outcome and adverse events. Notably, GA is more friendly and acceptable to the patients who are incapable of enduring intraoperative MER under LA.

Qian Kang, Wang Jiajing, Rao Jing, Zhang Peng, Sun Yaqiang, Hu Wenqing, Hao Jie, Jiang Xiaobing, Fu Peng

2023

“Parkinsons disease”, general anesthesia, local anesthesia, microelectrode recording, subthalamic nucleus deep brain stimulation

In Journal of the National Medical Association

Tabassum Hina, Iqbal Muhammad Mutahir, Mahmood Zafar, Parveen Maqsooda, Ullah Irfan

2023-Mar-11

Anthropometric measurements, Boosting tree, Decision rule sets

In Academic radiology

RATIONALE AND OBJECTIVES : Early prostate cancer detection and staging from MRI is extremely challenging for both radiologists and deep learning algorithms, but the potential to learn from large and diverse datasets remains a promising avenue to increase their performance within and across institutions. To enable this for prototype-stage algorithms, where the majority of existing research remains, we introduce a flexible federated learning framework for cross-site training, validation, and evaluation of custom deep learning prostate cancer detection algorithms.

MATERIALS AND METHODS : We introduce an abstraction of prostate cancer groundtruth that represents diverse annotation and histopathology data. We maximize use of this groundtruth if and when they are available using UCNet, a custom 3D UNet that enables simultaneous supervision of pixel-wise, region-wise, and gland-wise classification. We leverage these modules to perform cross-site federated training using 1400+ heterogeneous multi-parameteric prostate MRI exams from two University hospitals.

RESULTS : We observe a positive result, with significant improvements in cross-site generalization performance with negligible intra-site performance degradation for both lesion segmentation and per-lesion binary classification of clinically-significant prostate cancer. Cross-site lesion segmentation performance intersection-over-union (IoU) improved by 100%, while cross-site lesion classification performance overall accuracy improved by 9.5-14.8%, depending on the optimal checkpoint selected by each site.

CONCLUSION : Federated learning can improve the generalization performance of prostate cancer detection models across institutions while protecting patient health information and institution-specific code and data. However, even more data and participating institutions are likely required to improve the absolute performance of prostate cancer classification models. To enable adoption of federated learning with limited re-engineering of federated components, we open-source our FLtools system at https://federated.ucsf.edu, including examples that can be easily adapted to other medical imaging deep learning projects.

Rajagopal Abhejit, Redekop Ekaterina, Kemisetti Anil, Kulkarni Rushikesh, Raman Steven, Sarma Karthik, Magudia Kirti, Arnold Corey W, Larson Peder E Z

2023-Mar-11

Cancer classification, Deep learning, Federated learning, Gleason scores, Prostate MRI

In Clinical radiology

AIM : To develop and validate a nomogram model that combines computed tomography (CT)-based radiological factors extracted from deep-learning and clinical factors for the early predictions of immune checkpoint inhibitor-related pneumonitis (ICI-P).

MATERIALS AND METHODS : Forty ICI-P patients and 101 patients without ICI-P were divided randomly into the training (n=113) and test (n=28) sets. The convolution neural network (CNN) algorithm was used to extract the CT-based radiological features of predictable ICI-P and calculated the CT score of each patient. A nomogram model to predict the risk of ICI-P was developed by logistic regression.

RESULTS : CT score was calculated from five radiological features extracted by the residual neural network-50-V2 with feature pyramid networks. Four predictors of ICI-P in the nomogram model included a clinical feature (pre-existing lung diseases), two serum markers (absolute lymphocyte count and lactate dehydrogenase), and a CT score. The area under curve of the nomogram model in the training (0.910 versus 0.871 versus 0.778) and test (0.900 versus 0.856 versus 0.869) sets was better than the radiological and clinical models. The nomogram model showed good consistency and better clinical practicability.

CONCLUSION : The nomogram model that combined CT-based radiological factors and clinical factors can be used as a new non-invasive tool for the early prediction of ICI-P in lung cancer patients after immunotherapy with low cost and low manual input.

Cheng M, Lin R, Bai N, Zhang Y, Wang H, Guo M, Duan X, Zheng J, Qiu Z, Zhao Y

2023-Jan-14

In Fa yi xue za zhi

Liu Yang, Xu Min-Min, Zhang Ya, Liu Shi-Quan, Yuan Mei-Qing, Jia Zhen-Jun

2022-Dec-25

body fluid, forensic medicine, microbial genomics, microbiology, review, sexual assault

In The journal of evidence-based dental practice

ARTICLE TITLE AND BIBLIOGRAPHIC INFORMATION : Artificial intelligence applications in restorative dentistry: A systematic review. Revilla-León, M., Gómez-Polo, M., Vyas, S., Barmak, A. B., Özcan, M., Att, W., & Krishnamurthy, V. R. J Prosthet Dent 2021 SOURCE OF FUNDING: Not reported.

TYPE OF STUDY/DESIGN : Systematic review.

Alqutaibi Ahmed Yaseen, Aboalrejal Afaf Noman

2023-Mar

AI, Artificial intelligence, Dental caries, Machine learning, Restorative dentistry, Root fracture