Objective Metrics Research Articles

Comparative Techno-Economic Evaluation of a Standalone Solar Power System for Scaled Implementation in Off-Grid Areas

The increasing environmental concerns and dependence on fossil fuel-based energy sectors necessitate a shift towards renewable energy. Off-grid communities can particularly benefit from standalone, scaled renewable power plants. This study developed a comprehensive techno-economic framework, analyzed the objective metrics, and assessed the influence of economies of scale in solar PV power plants to electrify off-grid communities, taking Baluchistan, Pakistan, as a pilot case. Simulations and analyses were performed using the System Advisor Model (SAM). The results indicate a noteworthy reduction in the levelized cost of energy (LCOE) with increased power generation capacity. It was observed that utilizing bi-facial modules with single-axis tracking leads to a more cost-effective LCOE compared to the relatively expensive dual-axis trackers. The main cost factors identified in the analysis were capital costs, installed balance of plant (BOP), mechanical, and electrical costs. Notably, the disparity between the highest and lowest LCOE values across the six different power generation pathways amounted to approximately 38.5%. The average LCOE was determined to be 2.14 USD/kWh for fixed-mounted plants, 1.79 USD/kWh for single-axis plants, and 1.74 USD/kWh for dual-axis plants across the examined power generation capacity range. The findings can serve as a valuable benchmark, specifically for regional key stakeholders, in making informed investment decisions, formulating effective policies, and devising appropriate strategies for off-grid electrification and the development of renewable energy value chains.

Thermal Pulsation in the Management of Meibomian Gland Dysfunction and Dry Eye: A Report by the American Academy of Ophthalmology

To review the literature to determine the efficacy and safety of thermal pulsation technologies in improving signs or symptoms of meibomian gland dysfunction (MGD) and dry eye compared with no therapy or with conventional warm compress therapy or eyelid hygiene. A literature search was conducted in the PubMed database in June 2022 and again in March 2023 to identify all studies in the English language on the use of thermal pulsation to treat MGD or dry eye. The search yielded 59 citations, and 11 articles metall of the inclusion criteria. The panel methodologist then assigned a level of evidence rating for each study; 8 studies were rated level I evidence and 3 studies were rated level II evidence. All included studies evaluated a single 12-minute session using the LipiFlow automated thermal pulsation system (TearScience, Inc, or Johnson & Johnson). Improvements were detected in subjective and objective metrics of MGD or dry eye in patients within 1 to 12 months of thermal pulsation treatment compared with nontreatment. Most of the studies (9/11) reported greater efficacy with thermal pulsation than with standard warm compress therapy and eyelid hygiene. Four of these studies showed relevant industry conflicts of interest. Two of the 4 level I studies without direct industry participation concluded that thermal pulsation treatment was not significantly different from conventional hygiene or warm compress therapy control treatments (in symptoms in one of the studies and in objective findings in the second study). No serious adverse events were reported in any of the 11 studies. According to the current literature, a single thermal pulsation session may improve subjective or objective parameters of MGD and dry eye safely. However, industry support and participation were present in 4 of the 8 level I studies. The durability beyond several months and cost efficacy remain uncertain. Because the inclusion parameters of this assessment captured only the LipiFlow system, the conclusions are limited to that product. High-quality independent studies are needed to assess the long-term benefits of this intervention. The author(s) have no proprietary or commercial interest in any materials discussed in this article.

“Are They Listening, and Do They Find It Useful?”—Evaluation of Mid-Rotation Formative Subjective and Objective Feedback to Radiology Trainees

Residents commonly receive only end-of-rotation evaluations and thus are often unaware of their progress during a rotation. In 2021, our neuroradiology section instituted mid-rotation feedback in which rotating residents received formative subjective and objective feedback. The purpose of this study was to describe our feedback method and to evaluate if residents found it helpful. Radiology residents rotate 3-4 times on the neuroradiology service for 1-month blocks. At the midpoint of the rotation (2 weeks), 7-10 neuroradiology attendings discussed the rotating residents' subjective performance. One attending was tasked with facilitating this discussion and taking notes. Objective metrics were obtained from our dictation software. Compiled feedback was relayed to residents via email. A 16-question anonymous survey was sent to 39 radiology residents (R1-R4) to evaluate the perceived value of mid-rotation feedback. Odds ratios and 95% confidence intervals were computed using logistic regression. Sixty-nine percent (27/39) of residents responded to the survey; 92.6% (25/27) of residents reported receiving mid-rotation feedback in ≥50% of neuroradiology rotations; 92.3% (24/26) of residents found the subjective feedback helpful; 88.4% (23/26) of residents reported modifying their performance as suggested (100% R1-R2 vs 70% R3-R4; OR: 15.4 CI:1.26, >30.0);59.1% (13/22) of residents found the objective metrics helpful (75% R1-R2 vs 40% R3-R4; OR: 3.92 CI:0.74, 24.39) and 68.2% (15/22) stated they modified their performance based on these metrics (83.3% R1-R2 vs 50.0% R3-R4; OR:4.2 CI:0.73, 30.55); and 84.6% (22/26) of residents stated that mid-rotation subjective feedback and 45.5% (10/22) stated that mid-rotation objective feedback should be implemented in other sections. Majority of residents found mid-rotation feedback to be helpful in informing them about their progress and areas for improvement in the neuroradiology rotation, more so for subjective feedback than objective feedback. The majority of residents stated all rotations should provide mid-rotation subjective feedback.

Key factors for residency interview selection from the National Resident Matching Program: analysis of residency Program Director surveys, 2016-2020.

As the number of medical school graduates continues to outpace the available residency training positions, applying for residency in the United States has become a highly competitive process, often associated with a low rate of selection and invitation for interview. The National Resident Matching Program (NRMP) Program Director survey provides data assessing factors considered by Program Directors (PD) in selecting and inviting candidates for interview. Assessing the evolution of these factors over time is efficacious to inform and guide prospective applicants toward improving preparation for residency application. We aim to synthesize NRMP data showing factors that PDs reported and rated as important in their decision to select and invite applicants for interview. Data from residency PD surveys from 2008 to 2021were accessed, but after applying inclusion/exclusion criteria, only the data from 2016 to 2020 were reviewed and analyzed. The NRMP survey reports provided two metrics that characterized PDs' evaluation of the residency factors for interview, namely, "percent citing factor" and "average rating" on a 0 to 5 Likert-type scale. These two metrics were combined into an aggregate measure of importance (AI), and another measure of relative importance (RI) was constructed from normalizing the AI of each individual factor to the sum of the AI within each survey year. The top ranked factors were United States Medical Licensing Examination (USMLE) Step 1/Comprehensive Osteopathic Medical Licensing Examination (COMLEX) Level 1, Letter of Recommendation (LOR) in the specialty, Medical Student Performance Evaluation (MSPE/Dean's Letter), and USMLE Step 2 Clinical Knowledge (CK)/COMLEX Level 2 Cognitive Exam (CE) score, any failed attempt in USMLE/COMLEX, and perceived commitment to specialty. Factors rising in importance were Audition Elective/Rotation Within Your Department, Personal Statement (PS), Perceived Commitment to Specialty, Perceived Interest in Program, LOR in the Specialty, Other Life Experience, and Personal Prior Knowledge of the Applicant. Factors with declining importance were Interest in Academic Career, Awards or Special Honors in Basic Sciences, Graduate of Highly Regarded US Medical School, Awards or Special Honors in Clinical Clerkships, Lack of Gaps in Medical Education, Awards or Special Honors in Clerkship in Desired Specialty, and Consistency of Grades. Compared to the 2021PD survey, our findings show continued predictive consistency, particularly related to specialty and program commitment. The factors identified for the selection of medical school graduates for interview into a residency program reveal that PDs move toward a more integrated approach. Specifically, PDs are placing increasing emphasis on factors that border on subjective qualities more so than the more traditional, quantitative, and objective metrics. Medical students and educators need to continually apprise themselves of the NRMP data to inform students' preparation endeavors throughout medical school to strengthen their application portfolios and enhance their competitiveness for the matching process.

Low-Light Image Enhancement Method for Electric Power Operation Sites Considering Strong Light Suppression

Insufficient light, uneven light, backlighting, and other problems lead to poor visibility of the image of an electric power operation site. Most of the current methods directly enhance the low-light image while ignoring local strong light that may appear in the electric power operation site, resulting in overexposure and a poor enhancement effect. Aiming at the above problems, we propose a low-light image enhancement method for electric power operation sites by considering strong light suppression. Firstly, a sliding-window-based strong light judgment method was designed, which used a sliding window to segment the image, and a brightness judgment was performed based on the average value of the deviation and the average deviation of the subimages of the grayscale image from the strong light threshold. Then, a light effect decomposition method based on a layer decomposition network was used to decompose the light effect of RGB images with the presence of strong light and eliminate the light effect layer. Finally, a Zero-DCE (Zero-Reference Deep Curve Estimation) low-light enhancement network based on a kernel selection module was constructed to enhance the low-light images with reduced or no strong light interference. Comparison experiments using the electric power operation private dataset and the SICE (Single Image Contrast Enhancement) Part 2 public dataset showed that our proposed method outperformed the current state-of-the-art low-light enhancement methods in terms of both subjective visual effects and objective evaluation metrics, which effectively improves the image quality of electric power operation sites in low-light environments and provides excellent image bases for other computer vision tasks, such as the estimation of operators’ posture.

Quality indicators in Barrett's endoscopy: Best is yet to come.

There is growing interest in establishing quality indicators (QIs) for endoscopic screening and surveillance in Barrett's esophagus (BE). QIs are objective, measurable, and evidence-based metrics that are applicable in a health-care setting to monitor a process and identify key performance indicators (KPIs) to achieve defined goals. In the Barrett's endoscopy setting, QIs can offer a standardized approach to monitor and maintain high-quality endoscopy for BE screening and surveillance that will allow measuring performance of an endoscopist as an individual, a group, or a facility. Since BE is an endoscopically identifiable premalignant condition with histological corroboration, adherence to QIs is paramount for the early and accurate detection of dysplasia and neoplasia. It is the holy grail for BE screening and surveillance. Although several suggested QIs for Barrett's endoscopy exist, issues remain in determining the most appropriate ones. These issues include inconsistent use of terminology, unclear definitions, and a scarcity of studies linking these QIs with relevant patient outcomes, making it difficult for clinicians to understand the concept and clinical importance. Hence, there is an urgent need to determine what should constitute appropriate QIs for Barrett's endoscopy, clearly define items used in the QIs, and identify ways to measure these KPIs. Ultimately, well-defined and validated QIs will contribute to clinically effective, safe, timely, and patient-focused care. In this review, we summarize recent literature and discuss four proposed QIs: (i) neoplasia detection rate; (ii) postendoscopy Barrett's neoplasia; (iii) Barrett's inspection time; and (iv) adherence to the Seattle biopsy protocol.

SP11.3 Utilising an Accelerated Delphi Process to Develop Consensus on The Requirement and Components of a Pre-procedural Core Robotic Surgery Curriculum

Abstract Aim Robot-assisted Surgery (RAS) continues to grow globally. Despite this, in the UK and Ireland, it is estimated that over 70% of surgical trainees across all specialities have no access to robot-assisted surgical training (RAST). This study aimed to provide educational bodies guidance on a pre-procedural core robotic surgery curriculum (PPCRC) from the perspective of the end user; the surgical trainee. Methods The study was conducted in four Phases: P1: A steering group was formed and a systematic review conducted P2: Pan-Specialty Trainee Panel Virtual Classroom Discussion, P3: Accelerated Delphi Process and P4: Formulation of Recommendations. Forty-three surgeons in training representing all surgical specialties and training levels contributed to a three-round Delphi process. Additions to the second- and third-round surveys were formulated based on the answers and comments from previous rounds. Consensus opinion was defined as ≥80% agreement. Results There was 100% response from all three rounds. The formulated guidance showed good internal consistency (Cronbach alpha >0.8). There was 97.7% agreement that a standardised PPCRC would be advantageous to training and that, independent of speciality, there should be a common approach (95.5% agreement). Consensus was reached in multiple areas: 1. Experience and Exposure, 2. Access and context, 3. Curriculum Components, 4 Target Groups and Delivery, 5. Objective Metrics, Benchmarking and Assessment. Conclusions Using the Delphi methodology, multispecialty consensus among trainees to develop and reach content validation for the requirements and components of a PPCRC in the UK and Ireland was achieved. This guidance will benefit from further validation following implementation.

152. Walking time and grip strength as objective outcome metrics in the adult spinal deformity patient: series of 208 patients

152. Walking time and grip strength as objective outcome metrics in the adult spinal deformity patient: series of 208 patients

TaBE: Decoupling spatial and spectral processing with Taylor’s unfolding method in the beamspace domain for multi-channel speech enhancement

In recent years, significant advancements have been made in neural beamforming, leveraging spectral and spatial cues to enhance their performance in multi-channel speech enhancement. When the frame-wise processing mechanism is required, there exists a trade-off between performance and algorithmic delay for existing all-neural beamformers. However, from the perspective of multi-source information fusion, the network is often encapsulated into a black box to entangle and fuse the spatial and spectral features into a non-linear feature space, which hinders our understanding of how they work collaboratively for target speech extraction. In this regard, this paper proposes to decouple the spatial and spectral domain processing inspired by Taylor’s approximation theory. Specifically, we reformulate the time-variant beamforming defined in the spatial domain into the adaptive weighting and mixing of different beam components in the beamspace domain. This reformulation enables us to model the recovery of target speech as a weighted sum operation in the beamspace domain, where each beam component is associated with an introduced unknown term for residual interference cancellation. By virtue of Taylor’s series expansion, the recovery process can be decomposed into the superimposition of the 0th-order non-derivative and high-order derivative terms, where the former acts as spatial filtering in the spatial domain, and the latter serves as a residual interference canceller in the spectral domain. We conduct extensive experiments on the spatialized LibriSpeech and L3DAS Challenge datasets. Experimental results show that, compared with existing advanced approaches, the proposed method not only achieves competitive performance in terms of multiple objective metrics but also provides feasible guidance in multi-channel speech enhancement pipeline design.

Measuring hand movement for suturing skill assessment: A simulation-based study

BackgroundTo maximize patient safety, surgical skills education is increasingly adopting simulation-based curricula for formative skills assessment and training. However, many standardized assessment tools rely on human raters for performance assessment, which is resource-intensive and subjective. Simulators that provide automated and objective metrics from sensor data can address this limitation. We present an instrumented bench suturing simulator, patterned after the clock face radial suturing model from the Fundamentals of Vascular Surgery, for automated and objective assessment of open suturing skills. MethodsFor this study, 97 participants (35 attending surgeons, 32 residents, and 30 novices) were recruited at national vascular conferences. Automated hand motion metrics, especially focusing on rotational motion analysis, were developed from the inertial measurement unit attached to participants’ hands, and the proposed suite of metrics was used to differentiate between the skill levels of the 3 groups. ResultsAttendings’ and residents’ performances were found to be significantly different from novices for all metrics. Moreover, most of our novel metrics could successfully distinguish between finer skill differences between attending and resident groups. In contrast, traditional operative skill metrics, such as time and path length, were unable to distinguish attendings from residents. ConclusionThis study provides evidence for the effectiveness of rotational motion analysis in assessing suturing skills. The suite of inertial measurement unit-based hand motion metrics introduced in this study allows for the incorporation of hand movement data for suturing skill assessment.

A User Study on the Comparison of View Interfaces for VR-AR Communication in XR Remote Collaboration

Previous studies have investigated which context-sharing interfaces are effective in improving task performance; however, consistent results have yet to be found. In this study, we developed a convenient remote collaboration system that provides multiple context-sharing interfaces in a single platform (2D video, 360° video, and 3D reconstruction view). All interfaces can reflect live updates of environmental changes, and we aimed to clarify the effect of the interface on the quality of remote collaboration. Thirty participants were recruited to perform a target-finding-and-placing scenario. Using both objective and subjective metrics, we compared the task performance among the three interface conditions. The results showed that participants completed the task faster and reported a better collaborative experience in the 3D interface condition. Moreover, we found a strong preference for the 3D view interface. These results suggest that providing 3D reconstructed spatial information can enable remote experts to instruct local workers more effectively.

Exploring Multi-Stage GAN with Self-Attention for Speech Enhancement

Multi-stage or multi-generator generative adversarial networks (GANs) have recently been demonstrated to be effective for speech enhancement. The existing multi-generator GANs for speech enhancement only use convolutional layers for synthesising clean speech signals. This reliance on convolution operation may result in masking the temporal dependencies within the signal sequence. This study explores self-attention to address the temporal dependency issue in multi-generator speech enhancement GANs to improve their enhancement performance. We empirically study the effect of integrating a self-attention mechanism into the convolutional layers of the multiple generators in multi-stage or multi-generator speech enhancement GANs, specifically, the ISEGAN and the DSEGAN networks. The experimental results show that introducing a self-attention mechanism into ISEGAN and DSEGAN leads to improvements in their speech enhancement quality and intelligibility across the objective evaluation metrics. Furthermore, we observe that adding self-attention to the ISEGAN’s generators does not only improves its enhancement performance but also bridges the performance gap between the ISEGAN and the DSEGAN with a smaller model footprint. Overall, our findings highlight the potential of self-attention in improving the enhancement performance of multi-generator speech enhancement GANs.

Multistage progressive learning based speech enhancement using time-frequency attentive squeezed temporal convolutional networks

Speech enhancement (SE) is an important method for improving speech quality and intelligibility in noisy environments. An effective speech enhancement model depends on precise modelling of the long-range dependencies of noisy speech. Several recent studies have examined ways to enhance speech by capturing the long-term contextual information. For speech enhancement, the time-frequency (T-F) distribution of speech spectral components is also important, but is usually ignored in these studies. The multi-stage learning method is an effective way to integrate various deep-learning modules at the same time. The benefit of multi-stage training is that the optimization target can be iteratively updated stage by stage. In this paper speech enhancement is investigated by multi-stage learning using a multistage structure in which time-frequency attention (TFA) blocks are followed by stacks of squeezed temporal convolutional networks (S-TCN) with exponentially increasing dilation rates. To reinject original information into later stages, a feature fusion (FF) block is inserted at the input of later stages to reduce the possibility of speech information being lost in the early stages. The S-TCN blocks are responsible for temporal sequence modelling task. The time-frequency attention (TFA) is a simple but effective network module that explicitly exploits position information to generate a 2D attention map to characterise the salient T-F distribution of speech by using two branches, time-frame attention and frequency attention in parallel. A set of utterances from the LibriSpeech and Voicebank databases are used to evaluate the performance of the proposed SE. Extensive experiments have demonstrated that the proposed model consistently improves the performance over existing baselines across two widely used objective metrics such as PESQ and STOI. The average PESQ and STOI for proposed model are boosted by a factor of 41.7% and 5.4% for Libri speech dataset, 36.10% and 3.1% for Voice bank dataset as compared to noisy speech. Additionally, we explored the generalization of the proposed TFA-S-TCN model across different speech datasets through cross data base analysis. A significant improvement in system robustness to noise is also shown by our evaluation results using the TFA module.

Headpulse Biometric Measures Following Concussion in Young Adult Athletes

Concussions are common in sports. Return-to-play protocols can be enhanced by objective biometrics. To characterize temporal changes of headpulse, a digital biometric, in athletes with sports-related concussion; to explore the association of unstructured physical activity with headpulse changes. This cohort study included headpulse measurements from players in the highest level of amateur Australian Rules Football in South Australia. Analysis included feasibility and validation phases, with the feasibility cohort recruited between August 5, 2021, and September 10, 2021, and the validation cohort recruited between May 5, 2022, and September 3, 2022. Data were analyzed October 2022 through January 2023. Cranial accelerometry detected micromovements of the head following cardiac contraction (what we have described as "headpulse"). Headpulse was serially recorded for 1 month in concussed individuals. Headpulse waveforms underwent frequency transformation analysis per prespecified algorithm. Result Z scores were calculated. Headpulse Z scores exceeding 2 (2 SDs from control means) met an abnormality threshold. Headpulse sensitivity, timing, and duration of change were determined. A total of 59 control and 43 concussed individuals (44 total concussions; 1 control also concussed, 1 concussed individual injured twice) provided headpulse measurements. The feasibility cohort (all male) included 17 control (median [IQR] age, 23 [19-28] years) and 15 concussed individuals (median [IQR] age, 21 [19-23] years). The validation cohort included 25 female (median [IQR] age, 21 [20-22] years) and 17 male (median [IQR] age, 26 [23-29] years) control individuals, and 8 female (median [IQR] age, 28 [20-31] years) and 20 male (median [IQR] age, 21 [19-23] years) concussed individuals. Headpulse reached abnormality threshold in 26 of 32 concussed individuals (81%; 9% on day 0, 50% by day 2, 90% by day 14). Headpulse alterations lasted 14 days longer than symptoms and were exacerbated by return-to-play or unsupervised physical activity. In this study of 101 amateur Australian Rules Football athletes, the digital headpulse biometric was evaluated in 44 sports-related concussions. Compared with controls, new headpulse changes occurred after concussion; this objective metric may complement return-to-play protocols.

Advantages of Totally Stapled Collard Over Circular Stapled Technique for Cervical Esophagectomy Anastomosis

BackgroundThe quality of life between the totally stapled Collard (TSC) and circular stapled (CS) techniques from the patient’s perspective has not been assessed. This longitudinal patient-reported outcome and objective data assessment study aimed to compare the effects of TSC vs CS for cervical anastomosis with minimally invasive esophagectomy (MIE) on the quality of life. MethodsThis single-center, retrospective study included patients with esophageal cancer who underwent MIE and cervical anastomosis between April 2019 and February 2021; of whom, 105 and 216 patients received TSC and CS anastomosis, respectively. The objective metrics were evaluated based on postoperative complications. Patient-reported outcomes instruments using the European Organization for Research and Treatment of Cancer modules were assessed preoperatively. The Quality of Life Questionnaire (QLQ) Core 30 and the QLQ-Oesophagus Module 18 assessed the quality of life. Mixed-effects models examined changes in questionnaire scores. ResultsThe stenosis rate in the TSC group was significantly lower than that in the CS group. There were no significant differences in other postoperative complications between the groups. In the QLQ Core 30 scale, the TSC group had better emotional function, less fatigue, less appetite loss, and better function than the CS group, with significant differences in different time points between the groups. QLQ-Oesophagus Module 18 revealed that the TSC group had significantly less appetite loss, dysphagia, dry mouth, pain, and obstruction, and better eating and speech than the CS group. ConclusionsTSC has significant advantages over the CS technique in lowering postoperative symptom burden and improving quality of life for cervical anastomosis on patient-reported outcomes.

Keratoconus Detection-based on Dynamic Corneal Deformation Videos Using Deep Learning

To assess the performance of convolutional neural networks (CNNs) for automated detection of keratoconus (KC) in standalone Scheimpflug-based dynamic corneal deformation videos. Retrospective cohort study. We retrospectively analyzed datasets with records of 734 nonconsecutive, refractive surgery candidates, and patients with unilateral or bilateral KC. We first developed a video preprocessing pipeline to translate dynamic corneal deformation videos into 3-dimensional pseudoimage representations and then trained a CNN to directly identify KC from pseudoimages. We calculated the model's KC probability score cut-off and evaluated the performance by subjective and objective accuracy metrics using 2 independent datasets. Area under the receiver operating characteristics curve (AUC), accuracy, specificity, sensitivity, and KC probability score. The model accuracy on the test subset was 0.89 with AUC of 0.94. Based on the external validation dataset, the AUC and accuracy of the CNN model for detecting KC were 0.93 and 0.88, respectively. Our deep learning-based approach was highly sensitive and specific in separating normal from keratoconic eyes using dynamic corneal deformation videos at levels that may prove useful in clinical practice. Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Radiographic response to neoadjuvant therapy in pleural mesothelioma should serve as a guide for patient selection for cytoreductive operations.

Malignant pleural mesothelioma (MPM) is associated with poor prognosis despite advances in multimodal therapeutic strategies. While patients with resectable disease may benefit from added survival with oncologic resection, patient selection for mesothelioma operations often relies on both objective and subjective evaluation metrics. We sought to evaluate factors associated with improved overall survival (OS) in patients with mesothelioma who underwent macroscopic complete resection (MCR). Patients with MPM who received neoadjuvant therapy and underwent MCR were identified in a prospectively maintained departmental database. Clinicopathologic, blood-based, and radiographic variables were collected and included in a Cox regression analysis (CRA). Response to neoadjuvant therapy was characterized by a change in tumor thickness from pretherapy to preoperative scans using the modified RECIST criteria. In this study, 99 patients met the inclusion criteria. The median age of the included patients was 64.7 years, who were predominantly men, had smoking and asbestos exposure, and who received neoadjuvant therapy. The median change in tumor thickness following neoadjuvant therapy was -16.5% (interquartile range of -49.7% to +14.2%). CRA demonstrated reduced OS associated with non-epithelioid histology [hazard ratio (HR): 3.06, 95% confidence interval (CI): 1.62-5.78, p < 0.001] and a response to neoadjuvant therapy inferior to the median (HR: 2.70, CI: 1.55-4.72, p < 0.001). Patients who responded poorly (below median) to neoadjuvant therapy had lower median survival (15.8 months compared to 38.2 months, p < 0.001). Poor response to neoadjuvant therapy in patients with MPM is associated with poor outcomes even following maximum surgical cytoreduction and should warrant a patient-centered discussion regarding goals of care and may therefore help guide further therapeutic decisions.

Fuzzy Salt and Pepper Noise Removing for Color Images

Within the field of digital image processing applications, observed images frequently exposed to noise corruption stemming from image acquisition or transmission processes. This noise degradation reduces image quality and yields unfavorable outcomes in subsequent processing stages (e.g., segmentation, pattern recognition, and enhancement). Consequently, the mitigation of noise in images assumes paramount importance in the domain of image processing. This study introduces an algorithm centered around fuzzy logic for removing impulse noise from color images. The efficiency of the proposed algorithms is assessed by comparing their performance against various noise reduction methods. Objective metrics, namely peak signal-to-noise ratio and mean square error, substantiate that the proposed algorithms yield commendable outcomes in noise reduction and the preservation of intricate image details across a wide spectrum of noise densities

Implementation and Comparative Study of Pyramid-based Image Fusion Techniques for Lumbar Spine Images

Image fusion is a method of combining the necessary and relevant information from the set of source images into a single (fused) image which can be deemed to be more informative than the source. This paper discusses the implementation of various pyramid-based image fusion algorithms, such as the Laplacian pyramid, the ratio of the low-pass pyramid, the contrast pyramid, and the filter subtract decimate pyramid on multimodal CT and MR images of the lumbar spine. The fused images were evaluated using various objective evaluation quality metrics. The experimental results demonstrated that the ratio of the low pass pyramid achieved better performance compared to the other pyramids implemented, indicating that the fused image can also be used for further image fusion application or analysis purposes.

Auto‐segmentation of the clinical target volume using a domain‐adversarial neural network in patients with gynaecological cancer undergoing postoperative vaginal brachytherapy

AbstractPurposeFor postoperative vaginal brachytherapy (POVBT), the diversity of applicators complicates the creation of a generalized auto‐segmentation model, and creating models for each applicator seems difficult due to the large amount of data required. We construct an auto‐segmentation model of POVBT using small data via domain‐adversarial neural networks (DANNs).MethodsCT images were obtained postoperatively from 90 patients with gynaecological cancer who underwent vaginal brachytherapy, including 60 and 30 treated with applicators A and X, respectively. A basal model was devised using data from the patients treated with applicator A; next, a DANN model was constructed using these same 60 patients as well as 10 of those treated with applicator X through transfer learning techniques. The remaining 20 patients treated with applicator X comprised the validation set. The model's performance was assessed using objective metrics and manual clinical evaluation.ResultsThe DANN model outperformed the basal model on both objective metrics and subjective evaluation (p&lt;0.05 for all). The median DSC and 95HD values were 0.97 and 3.68 mm in the DANN model versus 0.94 and 5.61 mm in the basal model, respectively. Multi‐centre subjective evaluation by three clinicians showed that 99%, 98%, and 81% of CT slices contoured by the DANN model were acceptable versus only 73%, 77%, and 57% of those contoured by the basal model. One clinician deemed the DANN model comparable to manual delineation.ConclusionDANNs provides a realistic approach for the wide application of automatic segmentation of POVBT and can potentially be used to construct auto‐segmentation models from small datasets.