Manual Scoring Research Articles

Automated acute skin toxicity scoring in a mouse model through deep learning.

This study presents a novel approach to skin toxicity assessment in preclinical radiotherapy trials through an advanced imaging setup and deep learning. Skin reactions, commonly associated with undesirable side effects in radiotherapy, were meticulously evaluated in 160 mice across four studies. A comprehensive dataset containing 7542 images was derived from proton/electron trials with matched manual scoring of the acute toxicity on the right hind leg, which was the target area irradiated in the trials. This dataset was the foundation for the subsequent model training. The two-step deep learning framework incorporated an object detection model for hind leg detection and a classification model for toxicity classification. An observer study involving five experts and the deep learning model, was conducted to analyze the retrospective capabilities and inter-observer variations. The results revealed that the hind leg object detection model exhibited a robust performance, achieving an accuracy of almost 99%. Subsequently, the classification model demonstrated an overall accuracy of about 85%, revealing nuanced challenges in specific toxicity grades. The observer study highlighted high inter-observer agreement and showcased the model's superiority in accuracy and misclassification distance. In conclusion, this study signifies an advancement in objective and reproducible skin toxicity assessment. The imaging and deep learning system not only allows for retrospective toxicity scoring, but also presents a potential for minimizing inter-observer variation and evaluation times, addressing critical gaps in manual scoring methodologies. Future recommendations include refining the system through an expanded training dataset, paving the way for its deployment in preclinical research and radiotherapy trials.

Automated Evaluation Method for Risk Behaviors of Quay Crane Operators at Ports Using Virtual Reality

Currently, the operational risk assessment of quay crane operators at ports relies on manual evaluations based on experience, but this method lacks objectivity and fairness. As port throughput continues to grow, the port accident rate has also increased, making it crucial to scientifically evaluate the risk behaviors of operators and improve their safety awareness. This paper proposes an automated evaluation method based on a Deep Q-Network (DQN) to assess the risk behaviors of quay crane operators in virtual scenarios. A risk simulation module has been added to the existing automated quay crane remote operation simulation system to simulate potential risks during operations. Based on the collected data, a DQN-based benchmark model reflecting the operational behaviors and decision-making processes of skilled operators has been developed. This model enables a quantitative evaluation of operators’ behaviors, ensuring the objectivity and accuracy of the assessment process. The experimental results show that, compared with traditional manual scoring methods, the proposed method is more stable and objective, effectively reducing subjective biases and providing a reliable alternative to conventional manual evaluations. Additionally, this method enhances operators’ safety awareness and their ability to handle risks, helping them identify and avoid risks during actual operations, thereby ensuring both operational safety and efficiency.

Temporal and sleep stage-dependent agreement in manual scoring of respiratory events.

Obstructive sleep apnea diagnosis is based on the manual scoring of respiratory events. The agreement in the manual scoring of the respiratory events lacks an in-depth investigation as most of the previous studies reported only the apnea-hypopnea index or overall agreement, and not temporal, second-by-second or event subtype agreement. We hypothesized the temporal and subtype agreement to be low because the event duration or subtypes are not generally considered in current clinical practice. The data comprised 50 polysomnography recordings scored by 10 experts. The respiratory event agreement between the scorers was calculated using kappa statistics in a second-by-second manner. Obstructive sleep apnea severity categories (no obstructive sleep apnea/mild/moderate/severe) were compared between scorers. The Fleiss' kappa value for binary (event/no event) respiratory event scorings was 0.32. When calculated separately within N1, N2, N3 and R, the Fleiss' kappa values were 0.12, 0.23, 0.22 and 0.23, respectively. Binary analysis conducted separately for the event subtypes showed the highest Fleiss' kappa for hypopneas to be 0.26. In 34% of the participants, the obstructive sleep apnea severity category was the same regardless of the scorer, whereas in the rest of the participants the category changed depending on the scorer. Our findings indicate that the agreement of manual scoring of respiratory events depends on the event type and sleep stage. The manual scoring has discrepancies, and these differences affect the obstructive sleep apnea diagnosis. This is an alarming finding, as ultimately these differences in the scorings affect treatment decisions.

Clinical validation of an AI-based pathology tool for scoring of metabolic dysfunction-associated steatohepatitis.

Metabolic dysfunction-associated steatohepatitis (MASH) is a major cause of liver-related morbidity and mortality, yet treatment options are limited. Manual scoring of liver biopsies, currently the gold standard for clinical trial enrollment and endpoint assessment, suffers from high reader variability. This study represents the most comprehensive multisite analytical and clinical validation of an artificial intelligence (AI)-based pathology system, AI-based measurement of metabolic dysfunction-associated steatohepatitis (AIM-MASH), to assist pathologists in MASH trial histology scoring. AIM-MASH demonstrated high repeatability and reproducibility compared to manual scoring. AIM-MASH-assisted reads by expert MASH pathologists were superior to unassisted reads in accurately assessing inflammation, ballooning, MAS ≥ 4 with ≥1 in each score category and MASH resolution, while maintaining non-inferiority in steatosis and fibrosis assessment. These findings suggest that AIM-MASH could mitigate reader variability, providing a more reliable assessment of therapeutics in MASH clinical trials.

Automating hock wound detection in dairy cattle

Hock scoring in dairy cattle is a crucial welfare assessment tool used to evaluate the condition of a cow's hocks, particularly for signs of injury, swelling, or lesions. These scores provide insight into the overall well-being of the animals and are essential for ensuring proper management and housing conditions. Accurate hock scoring is vital because it can indicate issues such as poor bedding quality or inadequate space, which directly affect the health and productivity of the herd. Traditionally, hock scoring is performed manually by trained observers. However, consistency in scoring can be a challenge. Two studies were conducted to quantify inconsistency in hock scoring. In one study, manual scoring repeatability was measured. In the second study, manual and video scoring repeatability was measured. Repeatability was quantified with a weighted Cowen's kappa metric. Manual scoring was found to be inconsistent but more consistent than video scoring. This variability highlights the need for a more reliable, objective method of scoring. To address this, we explored the automation of hock score detection using artificial intelligence. Specifically, we employed a simple U-net semantic segmentation algorithm to detect wounds on the hocks without classifying them into specific categories. Automating the detection process can reduce observer bias, improve consistency, and allow for continuous monitoring of large herds. This approach holds promise for enhancing animal welfare by providing a more efficient and accurate method of assessing hock health in dairy cattle.

FAP and PSMA Expression by Immunohistochemistry and PET Imaging in Castration-Resistant Prostate Cancer: A Translational Pilot Study.

Prostate-specific membrane antigen (PSMA) is a theranostic target for metastatic prostate cancer (PCa). However, castration-resistant PCa (CRPC) may lose PSMA expression after systemic therapy. Fibroblast activation protein (FAP), expressed by carcinoma-associated fibroblasts in various cancer types, including PCa, has the potential to be an alternative target. In this study, we evaluated FAP expression in CRPC to assess its potential, using PSMA as a comparison. Methods: FAP expression was assessed using immunohistochemistry in 116 CRPC tumors: 78 adenocarcinomas, 11 small cell carcinomas, and 27 anaplastic carcinomas. Correlation analysis between manual scoring and automated scoring was performed on 54 whole-slide sections of metastatic CRPC. Paired FAP and PSMA stains were assessed in tissue microarray cores of CRPC (n = 62), consisting of locally advanced CRPC (n = 9) and metastatic CRPC (n = 53). FAP and PSMA positivity was defined by an immunohistochemistry score of at least 10. To explore the correlation of PSMA and FAP inhibitor (FAPi) PET imaging and immunohistochemistry, a preliminary analysis of 4 patients included in a [68Ga]-FAPi-46 imaging trial (NCT04457232) was conducted. Results: Manual and automated scoring of FAP yielded results with strong correlations. Overall, FAP expression in CRPC was notably lower than PSMA expression (median immunoscores, 14 vs. 72; P < 0.001). Different histologic subtypes of CRPC demonstrated distinct levels of PSMA expression, whereas their FAP expression levels were comparable. Among the 19 PSMA-negative tumors, 11 (58%) exhibited FAP positivity. FAP expression levels in lymph node metastases were significantly lower than those in nonnodal metastases (P = 0.021). Liver metastases showed significant enrichment of tumors with strong FAP expression compared with nonliver lesions (P = 0.016). In the 4 clinical trial patients, the biopsied metastatic lesions showed lower uptake on FAPi PET than on PSMA PET (median SUVmax, 9.6 vs. 14.5), consistent with FAP expression that was lower than PSMA expression in the corresponding tumor biopsy samples (median immunoscores, 30 vs. 160). Conclusion: Because of the low FAP expression levels in CRPC, the utility of FAPi PET imaging may be limited. Although FAPi PET imaging may be further tested in PSMA-negative CRPC, such as small cell carcinoma, other molecular imaging modalities should be evaluated as alternative choices.

Superior performance of a center-point AI model over VFLDNet in automated cobb angle estimation for scoliosis assessment.

Aims to establish the superiority of our proposed model over the state-of-the-art vertebra-focused landmark detection network (VFLDNet) in automating Cobb angle estimation from spinal radiographs. Utilizing a private dataset for external validation, we compared the performance of our center-point detection-based vertebra localization and tilt estimation network (VLTENet) with the key-point detection-based VFLDNet. Both models' Cobb angle predictions were rigorously evaluated against manual consensus score using metrics such as mean absolute error (MAE), correlation coefficient, intraclass correlation coefficient (ICC), Fleiss' kappa, Bland-Altman analysis, and classification metrics [sensitivity (SN), specificity, accuracy] focusing on major curve estimation and scoliosis severity classification. A retrospective analysis of 118 cases with 342 Cobb angle measurements revealed that our model achieved a MAE of 2.15° for total Cobb angles and 1.89° for the major curve, significantly outperforming VFLDNet's MAE of 2.80°and 2.57°, respectively. Both models demonstrated robust correlation and ICC, but our model excelled in classification consistency, particularly in predicting major curve magnitude (ours: kappa = 0.83; VFLDNet: kappa = 0.67). In subgroup analyses by scoliosis severity, our model consistently surpassed VFLDNet, displaying superior mean (SD) differences, narrower limits of agreement, and higher SN, specificity, and accuracy, most notably in moderate (ours: SN = 86.84%; VFLDNet: SN = 83.16%) to severe (ours: SN = 92.86%; VFLDNet: SN = 85.71%) scoliosis. Our model emerges as the superior choice for automated Cobb angle estimation, particularly in assessing major curve and moderate to severe scoliosis, underscoring its potential to revolutionize clinical workflows and enhance patient care.

Classifying histopathological growth patterns for resected colorectal liver metastasis with a deep learning analysis.

Histopathological growth patterns are one of the strongest prognostic factors in patients with resected colorectal liver metastases. Development of an efficient, objective and ideally automated histopathological growth pattern scoring method can substantially help the implementation of histopathological growth pattern assessment in daily practice and research. This study aimed to develop and validate a deep-learning algorithm, namely neural image compression, to distinguish desmoplastic from non-desmoplastic histopathological growth patterns of colorectal liver metastases based on digital haematoxylin and eosin-stained slides. The algorithm was developed using digitalized whole-slide images obtained in a single-centre (Erasmus MC Cancer Institute, the Netherlands) cohort of patients who underwent first curative intent resection for colorectal liver metastases between January 2000 and February 2019. External validation was performed on whole-slide images of patients resected between October 2004 and December 2017 in another institution (Radboud University Medical Center, the Netherlands). The outcomes of interest were the automated classification of dichotomous hepatic growth patterns, distinguishing between desmoplastic hepatic growth pattern and non-desmoplatic growth pattern by a deep-learning model; secondary outcome was the correlation of these classifications with overall survival in the histopathology manual-assessed histopathological growth pattern and those assessed using neural image compression. Nine hundred and thirty-two patients, corresponding to 3.641 whole-slide images, were reviewed to develop the algorithm and 870 whole-slide images were used for external validation. Median follow-up for the development and the validation cohorts was 43 and 29 months respectively. The neural image compression approach achieved significant discriminatory power to classify 100% desmoplastic histopathological growth pattern with an area under the curve of 0.93 in the development cohort and 0.95 upon external validation. Both the histopathology manual-scored histopathological growth pattern and neural image compression-classified histopathological growth pattern achieved a similar multivariable hazard ratio for desmoplastic versus non-desmoplastic growth pattern in the development cohort (histopathology manual score: 0.63 versus neural image compression: 0.64) and in the validation cohort (histopathology manual score: 0.40 versus neural image compression: 0.48). The neural image compression approach is suitable for pathology-based classification tasks of colorectal liver metastases.

AI-based algorithm for assessing coronary artery calcium score on contrast-enhanced cardiac computed tomography scans

Abstract Introduction In cardiac computed tomography (CT) examinations, non-contrast scans are often performed in addition to contrast-enhanced scans to quantify the coronary artery calcification (CAC) score. These calcium score scans are associated with additional exposure to ionizing radiation. Purpose We sought to develop a fully automated artificial intelligence (AI)-based algorithm capable of determining CAC score based solely on contrast-enhanced CT scans, eliminating the need for additional non-contrast scans. Methods An automated CAC scoring algorithm on contrast-enhanced CT scans was developed and trained using a dataset of 297 cardiac CT studies. Coronary artery calcifications were manually segmented in contrast-enhanced scans using a threshold of 2 standard deviations above the mean attenuation value of the ascending aorta. On non-contrast scans, CAC was manually assessed using a standard threshold of ≥130 Hounsfield Units. A correction factor for calcium score results assessed on contrast-enhanced scans was determined using linear regression. The algorithm was tested on an independent set of 90 contrast-enhanced CT scans (four manufacturers, eight scanner models). We compared automated CAC scores to manual expert reader reference assessments on non-contrast CT scans. CAC scores were categorized into four risk categories following the Society of Cardiovascular Computed Tomography recommendations: 0, 1-100, 101-300, and &amp;gt;300 Agatston Units. Results In the assessment of 90 CT studies (mean age 61.0±11.4 years, 46.7% males), the AI-model detected CAC in 69 contrast-enhanced scans (76.7%), comparable to the human reader's detection rate of CAC in 71 non-contrast scans (78.9%) (p = 0.63). The CAC score was initially calculated using AI-based segmentation of coronary calcification on contrast-enhanced scans and then the result was multiplied by established linear correction factor of 1.97. There was an excellent correlation between AI-model and manual reference total CAC scores (Pearson’s r = 0.96, 95% CI 0.94–0.97, p &amp;lt; 0.001). The model correctly classified 77 patients (85.6%) into the same CAC risk category as the human reader (Figure 1). Among 19 patients (21.1%) with a CAC score of zero, only 1 patient (5.3%) was reclassified with a non-zero CAC score by the AI-model. Cohen’s kappa value for CAC score risk categorization was 0.80 (p &amp;lt; 0.001), indicating very good agreement (Figure 2). Bland–Altman analysis revealed minimal bias of -9.7 Agatston Unit with 95% limits of agreement ranging from -184.8 to 165.5 Agatston Unit. Conclusions CAC score can be accurately quantified on contrast-enhanced cardiac CT scans using an automated AI-based algorithm. This approach has the potential to eliminate the necessity for an additional non-contrast CT scan, thereby reducing the patient's exposure to ionizing radiation.

Time–frequency ridge characterisation of sleep stage transitions: Towards improving electroencephalogram annotations using an advanced visualisation technique

Manual sleep stage scoring of polysomnography recordings is an expensive and time-consuming process, further complicated by inconsistent sleep stage agreement among sleep experts (clinicians and sleep technologists). Hence, development of automated sleep scoring algorithms are an emerging topic of interest. Automation typically mimics the clinical decision path by implementing a series of predefined rules, such as the American Academy of Sleep Medicine’s (AASM) scoring manual. Recently, data driven methods have emerged using machine or deep learning. Both manual and automated methods of scoring have known limitations; primarily, unacceptable variation in agreement between different scorers and algorithms. Within the literature, electroencephalogram (EEG) frequency is an important feature considered by both sleep experts and automated approaches for classifying sleep stages.This study presents a novel approach to sleep stage analysis, by developing a methodology to precisely determine the temporal location of sleep stage transitions. The current gold standard fails to identify such transitional changes, which leads to poor inter-scorer reliability. Therefore, development and implementation of such methodologies is a crucial, but overlooked, step in improving the consistency of scoring within sleep studies. In this work, EEG time–frequency ridge analysis was used to characterise the dominant frequency component of EEG signals in time, at the point of sleep stage transition. An in-depth analysis of N3 → N2 and N2 → N3 transitions in the 2018 PhysioNet challenge “You Snooze, You Win” and the Wisconsin Sleep Cohort (WSC) datasets (n = 994, n = 742; approximately 13,888 h of sleep data) showed consistent time–frequency patterns at the point of transition, from one sleep stage to another. This methodology allows simple and ‘interpretable’ features to be generated in future work, to precisely identify the temporal location of sleep stage transitions with the aim of improving inter-scorer reliability.

Assessment of PD-L1 expression and tumour infiltrating lymphocytes in early-stage non-small cell lung carcinoma with artificial intelligence algorithms

AimsTo study programmed death ligand 1 (PD-L1) expression and tumour infiltrating lymphocytes (TILs) in patients with early-stage non-small cell lung carcinoma (NSCLC) with artificial intelligence (AI) algorithms.MethodsThe study included samples...

EzySCR: A free and easy tool for scoring event-related skin conductance responses in the first, second, and third interval latency windows.

Skin conductance is a commonly used physiological measure during psychology experiments, such as during fear conditioning. Methods for scoring skin conductance responses (SCRs) are highly heterogeneous, though most researchers agree that manually inspected scores provide the highest quality data when compared to most available fully automated scoring methods. However, manual scoring is extremely time-consuming. We developed a semi-automated scoring program that reduces the time required to process SCR data at a level of quality akin to manual scoring. In contrast to all previous scoring programs, our program enables scoring of first interval response (FIR), second interval response (SIR), and third interval response (TIR) SCRs. Using interclass correlation coefficients (ICCs), Bland-Altman plots and Pareto analysis, we show here that our method is highly reliable and produces data that are almost identical to data that are manually scored and scored using LEDALAB. This software is very easy to use and is freely available to download and modify. We expect that this software will be helpful in reducing the time required to produce high quality FIR, SIR, and TIR skin conductance data for psychology researchers around the world.

Can multiplexing Immunohistochemistry for PD-L1, CD68, and CD3 improve scoring for “Keytruda®” Indication?

Abstract Introduction/Objective FDA-approved companion diagnostics immunohistochemistry scoring for PD-L1 is essential for Keytruda ® treatment in patients with NSCLC. The challenge with PD-L1 testing alone is false positive PD-L1 expression score due to alveolar macrophage infiltration. Multiplexing with PD-L1, CD68, and CD3 could help alleviate this issue. By viewing PD-L1 expression on tumor cells, CD68 expression on macrophages, and the presence of CD3+ T cells on a single slide, pathologists will be able to see the presence of an immune microenvironment (high PD-L1 expression with abundant macrophages) and the potential for response to other types of Immunotherapies (presence of T cells). Methods/Case Report Positive NSCLC FFPE blocks were sectioned and stained using Agilent Dako Omnis. Two combinations of multiplex staining were prepared for each block by staining PD-L1 (brown) alone first followed by CD3 and CD68. First slide: CD3(Vina Green), CD68(Magenta); Second slide: CD3(Magenta), CD68(Vina Green). To prevent wash out, dehydration and clearing occurred with two changes of 100% alcohol and three changes of xylene for manual cover slipping. Results (if a Case Study enter NA) Manual scoring of PD-L1 alone vs two different multiplex combinations was compared to Agilent Proscia digital AI scoring using a preliminary study set (9 positives, 2 negatives). CD68-green and CD3-magenta showed better discrimination than CD68-magenta and CD3-green at a glance. This could be due to the staining intensity and nonspecific background staining of CD68-magenta. Manual scoring was higher compared to digital scoring, which could be due to the human error of positive bias. Digital scoring was significantly lower for PD-L1 alone vs both multiplex stains. The digital scoring for the multiplex stains showed a higher scoring percentage by removing false positive macrophages. Conclusion Reflex scoring workflow is being developed to focus on difficult cases using the multiplex triple stain, i.e. clear negative and clear &amp;gt;50% with 3+ intensity. More cases will be collected and studied to further investigate the improved PD-L1 scoring with digital imaging assisted by artificial intelligence and to compare the multiplex color combinations.

Exploring Hand Function and Performance Disparities among Women in Varied Occupational Spheres: A Preliminary Report

Abstract Introduction The hand represents the most interactive aspect of the upper extremity. Previous research indicates that young individuals and females typically exhibit superior hand dexterity. Limited literature exists regarding the impact of occupations that require dexterous activities on hand function, potentially influencing the preservation of dexterity as individuals age. Objectives This study sought to assess the hand dexterity, grip strength, and pinch strength of female beedi rollers involved in intricate tasks with those of female nonbeedi workers across different age brackets. Materials and Methods This study in a community setting included a sample of 664 female participants, evenly distributed between two groups. The participants were further categorized into age brackets: 25 to 34, 35 to 44, 45 to 54, and 55 to 64 years. Dexterity, grip strength, and pinch strength were assessed using Purdue Pegboard, Jamar hand dynamometer, and pinch meter, respectively. Statistical Analysis Descriptive statistics with mean and standard deviation; analysis of variance and post hoc Bonferroni test were done for multiple comparisons. Results Manual dexterity scores, grip strength, and pinch strength were evaluated and recorded for all age groups among both beedi rollers and nonbeedi workers. Manual dexterity scores were higher in beedi rollers than nonbeedi workers for all age groups, with statistically significant differences (p &lt; 0.05). Similarly, grip strength and pinch strength scores were significantly greater in beedi rollers across all age groups (p &lt; 0.05), except for pinch strength in the nondominant hands of participants aged 35 to 44 and 45 to 54 years of nonbeedi workers, where the differences were not statistically significant (p &gt; 0.05). Conclusion Across all age brackets, beedi rollers exhibited superior manual dexterity, grip strength, and pinch strength compared with nonbeedi rollers. The elevated dexterity scores observed in beedi rollers aged 25 years and older suggest that occupations requiring dexterity may positively influence and maintain hand performance as individuals age, thus highlighting the potential benefits of dexterity-based occupations on aging.

Arousal burden is highest in supine sleeping position and during light sleep.

Arousal burden (AB) is defined as the cumulative duration of arousals during sleep divided by the total sleep time. However, in-depth analysis of AB related to sleep characteristics is lacking. Based on previous studies addressing the arousal index (ArI), we hypothesized that the AB would peak in the supine sleeping position and during non-rapid eye movement stage 1 (N1) and show high variability between scorers. Nine expert scorers analyzed polysomnography recordings of 50 participants, the majority with an increased risk for obstructive sleep apnea. AB was calculated in different sleeping positions and sleep stages. A generalized estimating equation was utilized to test the association between AB and sleeping positions, sleep stages, and scorers. The correlation between AB and ArI was tested with Spearman's rank-order correlation. AB significantly differed between sleeping positions (p<0.001). The median AB in the supine sleeping position was 47-62% higher than in the left and right position. The AB significantly differed between the sleep stages (p<0.001); the median AB was more than 200% higher during N1 than during other sleep stages. In addition, the AB differed significantly between scorers (p<0.001) and correlated strongly with ArI (r=0.935, p<0.001). AB depends on the sleeping position, sleep stage, and scorer as hypothesized. AB behaved similarly as the ArI, but the high variability in the ABs between scorers indicates a potential limitation caused by subjective manual scoring. Thus, the development of more accurate techniques for scoring arousals is required before AB can be reliably utilized.

Data science basis and influencing factors for the evaluation of environmental safety perception in Macau parishes

In the context of rapid urbanization, accurately identifying the visual factors that influence environmental safety perception is crucial for improving urban transportation environments and enhancing pedestrian safety. With the increase in urban population density and traffic flow, optimizing urban environmental design to elevate residents’ sense of safety has become a key issue in urban planning and management. However, the existing studies face numerous challenges in conducting large-scale quantitative analysis of environmental safety perception in complex scenarios, such as difficulties in data acquisition and limitations in analytical methods. This study addresses these challenges by applying image semantic segmentation and object detection techniques to extract key visual elements from street view images, combined with manual scoring and deep learning methods, to construct a road safety perception dataset. Using a LightGBM model and the SHAP interpretation framework, in this study, we identify the critical visual factors influencing environmental safety perception. An empirical study was conducted in Macau, a modern city where Eastern and Western cultures intersect, and tourism thrives. The findings reveal that: ① The overall environmental safety perception in the eight parishes and surrounding roads of Macau is relatively high, with significant regional differences in safety perception scores around Macau’s parish roads; ② The proportions of buildings, sidewalks, roads, and trees in images are the four primary factors influencing environmental safety perception; ③ The proportions and quantities of visual elements interact with each other, and their reasonable distribution helps form clear spatial visibility and creates conducive activity spaces, thereby enhancing the perception of environmental safety. Through empirical analysis, this study uncovers the mechanisms by which visual elements in urban street scenes affect environmental safety perception, providing scientific evidence for urban planning and transportation environment improvement. The research holds theoretical significance and offers practical references for urban design and management, demonstrating broad application value.

Performance of a HER2 testing algorithm tailored for urothelial bladder cancer: A Bi-centre study

AimsThis study aimed to develop an AI algorithm for automated HER2 scoring in urothelial bladder cancer (UBCa) and assess the interobserver agreement using both manual and AI-assisted methods based on breast cancer criteria. Methods and ResultsWe utilized 330 slides from two institutions for initial AI development and selected 200 slides for the ring study, involving six pathologists (3 senior, 3 junior). Our AI algorithm achieved high accuracy in two independent tests, with accuracies of 0.94 and 0.92. In the ring study, the AI-assisted method improved both accuracy (0.66 vs 0.94) and consistency (kappa=0.48; 95 % CI, 0.443–0.526 vs kappa=0.87; 95 % CI, 0.852–0.885) compared to manual scoring, especially in HER2-low cases (F1-scores: 0.63 vs 0.92). Additionally, in 62.3 % of heterogeneous HER2-positive cases, the interpretation accuracy significantly improved (0.49 vs 0.93). Pathologists, particularly junior ones, experienced enhanced accuracy and consistency with AI assistance. ConclusionsThis is the first study to provide a quantification algorithm for HER2 scoring in UBCa to assist pathologists in diagnosis. The ring study demonstrated that HER2 scoring based on breast cancer criteria can be effectively applied to UBCa. Furthermore, AI assistance significantly improves the accuracy and consistency of interpretations among pathologists with varying levels of experience, even in heterogeneous cases.

App-Based Volleyball Scoreboard Performance Evaluation

Study purpose. This research aims to develop and evaluate an application-based volleyball scoreboard game device designed to facilitate the process of recording and managing scores in real time. Materials and methods. This research uses the R&amp;D development method which aims to develop a device-based volleyball game scoreboard application. This research involved 3 IT experts, 5 referees who acted as evaluators of the initial product that had been developed. In addition, there were 5 samples in the small-scale test and 30 samples in the large-scale test. Results. The results of the analysis of the development of application-based volleyball scoreboard game devices by conducting feasibility / justification tests involving economic, technical, business and environmental aspects by refereeing experts, which obtained an average value of 91.60%, where the value was included in the very good category. While the results of the analysis of IT experts obtained an average value of 90.89%, where the value is included in the very good category. The results of the analysis of the effectiveness of the media development of the application-based volleyball scoreboard game device through a small scale test, which obtained an average value of 92.63%, which means it can be concluded that the development model of the application-based volleyball scoreboard game device that has been developed can be implemented/applied with a very good category. Meanwhile, the results of the large-scale test or broad-scale test obtained an average value of 90.94%, which means it can be concluded that the development model of the application-based volleyball scoreboard game device that has been developed is in the excellent category. Conclusion. In conclusion, this application-based volleyball scoreboard game device can be an effective solution to overcome the obstacles of manual score recording, while improving the quality of organising volleyball matches

Altered vocal communication in adult vasopressin-deficient Brattleboro rats

The neuropeptide, arginine vasopressin (AVP), has been implicated in social communication across a diverse array of species. Many rodents communicate basic behavioral states with negative versus positive valence through high-pitched vocalizations above the human hearing range (ultrasonic vocalizations; USVs). Previous studies have found that Brattleboro (Bratt) rats, which have a mutation in the Avp gene, exhibit deficits in their USVs from the early postnatal period through adolescence, but the magnitude of this effect appears to decrease from the juvenile to adolescent phase. The present study tested whether Bratt rats continue to exhibit USV deficits in adulthood. USVs of adult male and female Bratt and wild type (WT) rats were recorded in two contexts: a novel environment (empty arena) and a social context (arena filled with bedding soiled by same-sex conspecifics). The number, frequency, and duration of 50 kHz USVs were quantified by DeepSqueak after validation with manual scoring. Twenty-two kHz measures were quantified by manual scoring because DeepSqueak failed to accurately detect USVs in this frequency range. Adult Bratt rats did not exhibit deficits in the number of 50 kHz USVs: male Bratt rats emitted similar 50 kHz USVs as male WT rats, whereas female Bratt rats emitted more USVs than female WT rats. USV frequency and duration were altered in adult Bratt rats, but in a context-dependent manner. Twenty-two kHz USVs were less affected by the Bratt mutation. The present study demonstrates how chronic AVP deficiency impacts social communication across the lifespan. The present findings reveal a complex role for AVP in vocal communication, whereby disruption to the Avp gene leads to sex-, context-, and developmental phase-specific effects on the quantity and spectrotemporal characteristics of rat USVs.

Automatic ploidy prediction and quality assessment of human blastocysts using time-lapse imaging

Assessing fertilized human embryos is crucial for in vitro fertilization, a task being revolutionized by artificial intelligence. Existing models used for embryo quality assessment and ploidy detection could be significantly improved by effectively utilizing time-lapse imaging to identify critical developmental time points for maximizing prediction accuracy. Addressing this, we develop and compare various embryo ploidy status prediction models across distinct embryo development stages. We present BELA, a state-of-the-art ploidy prediction model that surpasses previous image- and video-based models without necessitating input from embryologists. BELA uses multitask learning to predict quality scores that are thereafter used to predict ploidy status. By achieving an area under the receiver operating characteristic curve of 0.76 for discriminating between euploidy and aneuploidy embryos on the Weill Cornell dataset, BELA matches the performance of models trained on embryologists’ manual scores. While not a replacement for preimplantation genetic testing for aneuploidy, BELA exemplifies how such models can streamline the embryo evaluation process.