A gradient mapping guided explainable deep neural network for extracapsular extension identification in 3D head and neck cancer computed tomography images.

BACKGROUND: Diagnosis and treatment management for head and neck squamous cell carcinoma (HNSCC) is guided by routine diagnostic head and neck computed tomography (CT) scans to identify tumor and lymph node features. The extracapsular extension (ECE) is a strong predictor of patients' survival outcomes with HNSCC. It is essential to detect the occurrence of ECE as it changes staging and treatment planning for patients. Current clinical ECE detection relies on visual identification and pathologic confirmation conducted by clinicians. However, manual annotation of the lymph node region is a required data preprocessing step in most of the current machine learning-based ECE diagnosis studies. PURPOSE: In this paper, we propose a Gradient Mapping Guided Explainable Network (GMGENet) framework to perform ECE identification automatically without requiring annotated lymph node region information. METHODS: The gradient-weighted class activation mapping (Grad-CAM) technique is applied to guide the deep learning algorithm to focus on the regions that are highly related to ECE. The proposed framework includes an extractor and a classifier. In a joint training process, informative volumes of interest (VOIs) are extracted by the extractor without labeled lymph node region information, and the classifier learns the pattern to classify the extracted VOIs into ECE positive and negative. RESULTS: In evaluation, the proposed methods are well-trained and tested using cross-validation. GMGENet achieved test accuracy and area under the curve (AUC) of 92.2% and 89.3%, respectively. GMGENetV2 achieved 90.3% accuracy and 91.7% AUC in the test. The results were compared with different existing models and further confirmed and explained by generating ECE probability heatmaps via a Grad-CAM technique. The presence or absence of ECE has been analyzed and correlated with ground truth histopathological findings. CONCLUSIONS: The proposed deep network can learn meaningful patterns to identify ECE without providing lymph node contours. The introduced ECE heatmaps will contribute to the clinical implementations of the proposed model and reveal unknown features to radiologists. The outcome of this study is expected to promote the implementation of explainable artificial intelligence-assiste ECE detection.

Thermal-porosity characterization data of additively manufactured Ti-6Al-4V thin-walled structure via laser engineered net shaping.

In-process thermal melt pool images and post-fabrication porosity labels are acquired for Ti-6Al-4V thin-walled structure fabricated with OPTOMEC Laser Engineered Net Shaping (LENS™) 750 system. The data is collected for nondestructive thermal characterization of direct laser deposition (DLD) build. More specifically, a Stratonics dual-wavelength pyrometer captures a top-down view of the melt pool of the deposition heat-affected zone (HAZ), which is above 1000∘C, and Nikon X-Ray Computed Tomography (XCT) XT H225 captures internal porosity reflective of lack of fusion during the fabrication process. The pyrometer images provided in Comma Separated Values (CSV) format are cropped to center the melt pool to temperatures above 1000℃, indicative of the shape and distribution of temperature values. Melt pool coordinates are determined using pyrometer specifications and thin wall build parameters. XCT porosity labels of sizes between 0.05 mm to 1.00 mm are registered within 0.5 mm of the melt pool image coordinate. An XCT porosity-labeled table provided in the Excel spreadsheet format contains time stamps, melt pool coordinates, melt pool eccentricity, peak temperature, peak temperature coordinates, pore size, and pore label. Thermal-porosity data utilization aids in generating data-driven quality control models for manufacturing parts anomaly detection.

Preoperative Prediction and Identification of Extracapsular Extension in Head and Neck Cancer Patients: Progress and Potential.

Background This study aimed to demonstrate both the potential and development progress in the identification of extracapsular nodal extension in head and neck cancer patients prior to surgery. Methodology A deep learning model has been developed utilizing multilayer gradient mapping-guided explainable network architecture involving a volume extractor. In addition, the gradient-weighted class activation mapping approach has been appropriated to generate a heatmap of anatomic regions indicating why the algorithm predicted extension or not. Results The prediction model shows excellent performance on the testing dataset with high values of accuracy, the area under the curve, sensitivity, and specificity of 0.926, 0.945, 0.924, and 0.930, respectively. The heatmap results show potential usefulness for some select patients but indicate the need for further training as the results may be misleading for other patients. Conclusions This work demonstrates continued progress in the identification of extracapsular nodal extension in diagnostic computed tomography prior to surgery. Continued progress stands to see the obvious potential realized where not only can unnecessary multimodality therapy be avoided but necessary therapy can be guided on a patient-specific level with information that currently is not available until postoperative pathology is complete.

Effectiveness of facemasks for opening a university campus in Mississippi, United States - a modelling study.

Background: Universities are at risk for COVID-19 and Fall semester begins in August 2020 for most campuses in the United States. The Southern States, including Mississippi, are experiencing a high incidence of COVID-19. Aims: The objective of this study is to model the impact of face masks and hybrid learning on the COVID-19 epidemic on Mississippi State University (MSU) campus. Methods: We used an age structured deterministic mathematical model of COVID-19 transmission within the MSU campus population, accounting for asymptomatic transmission. We modeled facemasks for the campus population at varying proportions of mask use and effectiveness, and Hyflex model of partial online learning with reduction of people on campus. Results: Facemasks can substantially reduce cases and deaths, even with modest effectiveness. Even 20% uptake of masks will halve the epidemic size. Facemasks combined with Hyflex reduces epidemic size even more. Conclusions: Universal use of face masks and reducing the number of people on campus may allow safer universities reopening.

Alarm-Related Workload in Default and Modified Alarm Settings and the Relationship Between Alarm Workload, Alarm Response Rate, and Care Provider Experience: Quantification and Comparison Study.

BACKGROUND: Delayed or no response to impending patient safety-related calls, poor care provider experience, low job satisfaction, and adverse events are all unwanted outcomes of alarm fatigue. Nurses often cite increases in alarm-related workload as a reason for alarm fatigue, which is a major contributor to the aforementioned unwanted outcomes. Increased workload affects both the care provider and the patient. No studies to date have evaluated the workload while caring for patients and managing alarms simultaneously and related it to the primary measures of alarm fatigue-alarm response rate and care provider experience. Many studies have assessed the effect of modifying the default alarm setting; however, studies on the perceived workload under different alarm settings are limited. OBJECTIVE: This study aimed to assess nurses' or assistants' perceived workload index of providing care under different clinical alarm settings and establish the relationship between perceived workload, alarm response rate, and care provider experience. METHODS: In a clinical simulator, 30 participants responded to alarms that occurred on a physiological monitor under 2 conditions (default and modified) for a given clinical condition. Participants completed a National Aeronautics and Space Administration-Task Load Index questionnaire and rated the demand experienced on a 20-point visual analog scale with low and high ratings. A correlational analysis was performed to assess the relationships between the perceived workload score, alarm response rate, and care provider experience. RESULTS: Participants experienced lower workloads when the clinical alarm threshold limits were modified according to patients' clinical conditions. The workload index was higher for the default alarm setting (57.60 [SD 2.59]) than for the modified alarm setting (52.39 [SD 2.29]), with a statistically significant difference of 5.21 (95% CI 3.38-7.04), t28=5.838, P<.05. Significant correlations were found between the workload index and alarm response rate. There was a strong negative correlation between alarm response rate and perceived workload, ρ28=-.54, P<.001 with workload explaining 29% of the variation in alarm response rate. There was a moderate negative correlation between the experience reported during patient care and the perceived workload, ρ28=-.49, P<.05. CONCLUSIONS: The perceived workload index was comparatively lower with alarm settings modified for individual patient care than in an unmodified default clinical alarm setting. These findings demonstrate that the modification of clinical alarm limits positively affects the number of alarms accurately addressed, care providers' experience, and overall satisfaction. The findings support the removal of nonessential alarms based on patient conditions, which can help care providers address the remaining alarms accurately and provide better patient care.