The Use of E-Learning in Peyton's 4-Step Approach: Evaluation of Facial Computed Tomography Scans.

Imparting procedural skills is challenging. Peyton's approach is an effective face-to-face teaching technique increasingly used in complex skills training. Institutions are beginning to incorporate online training as part of their procedural curriculum. We developed E-Peyton's to employ Peyton's approach through an electronic learning platform. The efficacy of E-Peyton's approach in teaching the interpretation of facial computed tomography (CT) scans is evaluated in this study. Naïve learners (n=41) were randomized into 2 groups based on teaching techniques employed: E-Peyton's (n=20) and Peyton's (n=21) approaches. The distance between the infraorbital margin and the posterior ledge was measured using a 3-part standardized measuring protocol on OsiriX. Twenty measurements were assessed for accuracy against the benchmark (±2 mm) at week 0 and week 1. Training durations were compared. Questionnaires were administered before and after the study to identify learners' acceptance of teaching techniques and their confidence in interpreting facial CT scans. Learners in both teaching techniques had comparable skills retention. Gap scores indicate significant improvement in learner's confidence levels regardless of teaching technique (P<0.05). Both teaching techniques were well-accepted by learners. E-Peyton's and Peyton's approaches required a similar training duration. The COVID-19 pandemic highlights the importance of effective remote learning platforms. E-Peyton's approach is comparable to that of Peyton's in all areas of assessment. E-Peyton's approach effectively automates Peyton's approach, allowing for standardized, high-quality procedural skills training while reducing manpower burden.

Mapping the Posterior Ledge and Optic Foramen in Orbital Floor Blowout Fractures.

Background The posterior ledge (PL) is a vital structure that supports the implant posteriorly during orbital floor reconstruction. This study describes a technique for mapping the PL in relation to the infraorbital margin (IM) in patients with orbital floor blowout fractures. This study establishes the location of the optic foramen in relation to the PL. Methods Facial computed tomography (FCT) scans of 67 consecutive patients with isolated orbital floor blowout fractures were analyzed using Osirix. Planes of reference for orbital fractures, a standardized technique for performing measurements on FCT, was used. Viewed coronally, the orbit was divided into seven equal sagittal slices (L1 laterally to L7 medially) with reference to the midorbital plane. The distances of PL from IM and location of optic foramen were determined. Results The greatest distance to PL is found at L5 (median: 30.1 mm, range: 13.5-37.1 mm). The median and ranges for each slice are as follows: L1 (median: 0.0 mm, range: 0.0-19.9 mm), L2 (median: 0.0 mm, range: 0.0-21.5 mm), L3 (median: 15.8 mm, range: 0.0-31.7 mm), L4 (median: 26.1 mm, range: 0.0-34.0 mm), L5 (median: 30.1 mm, range: 13.5-37.1 mm), L6 (median: 29.0 mm, range: 0.0-36.3 mm), L7 (median: 20.8 mm, range: 0.0-39.2 mm). The median distance of the optic foramen from IM is 43.7 mm (range: 37.0- 49.1) at L7. Conclusion Distance to PL from IM increases medially until the L5 before decreasing. A reference map of the PL in relation to the IM and optic foramen is generated. The optic foramen is located in close proximity to the PL at the medial orbital floor. This aids in preoperative planning and intraoperative dissection.

A promising deep learning-assistive algorithm for histopathological screening of colorectal cancer.

Colorectal cancer is one of the most common cancers worldwide, accounting for an annual estimated 1.8 million incident cases. With the increasing number of colonoscopies being performed, colorectal biopsies make up a large proportion of any histopathology laboratory workload. We trained and validated a unique artificial intelligence (AI) deep learning model as an assistive tool to screen for colonic malignancies in colorectal specimens, in order to improve cancer detection and classification; enabling busy pathologists to focus on higher order decision-making tasks. The study cohort consists of Whole Slide Images (WSI) obtained from 294 colorectal specimens. Qritive's unique composite algorithm comprises both a deep learning model based on a Faster Region Based Convolutional Neural Network (Faster-RCNN) architecture for instance segmentation with a ResNet-101 feature extraction backbone that provides glandular segmentation, and a classical machine learning classifier. The initial training used pathologists' annotations on a cohort of 66,191 image tiles extracted from 39 WSIs. A subsequent application of a classical machine learning-based slide classifier sorted the WSIs into 'low risk' (benign, inflammation) and 'high risk' (dysplasia, malignancy) categories. We further trained the composite AI-model's performance on a larger cohort of 105 resections WSIs and then validated our findings on a cohort of 150 biopsies WSIs against the classifications of two independently blinded pathologists. We evaluated the area under the receiver-operator characteristic curve (AUC) and other performance metrics. The AI model achieved an AUC of 0.917 in the validation cohort, with excellent sensitivity (97.4%) in detection of high risk features of dysplasia and malignancy. We demonstrate an unique composite AI-model incorporating both a glandular segmentation deep learning model and a classical machine learning classifier, with excellent sensitivity in picking up high risk colorectal features. As such, AI plays a role as a potential screening tool in assisting busy pathologists by outlining the dysplastic and malignant glands.

Factors associated with self-reported burnout level in allied healthcare professionals in a tertiary hospital in Singapore.

BACKGROUND: Burnout has adverse implications in healthcare settings, compromising patient care. Allied health professionals (AHPs) are defined as individuals who work collaboratively to deliver routine and essential healthcare services, excluding physicians and nurses. There is a lack of studies on burnout among AHPs in Singapore. This study explored factors associated with a self-reported burnout level and barriers to seeking psychological help among AHPs in Singapore. METHODS: We conducted a cross-sectional study in a sample of AHPs in a tertiary hospital from October to December 2019. We emailed a four-component survey to 1127 eligible participants. The survey comprised four components: (1) sociodemographic characteristics, (2) Maslach Burnout Inventory (MBI-HSS), (3) Areas of Worklife Survey, and (4) Perceived Barriers to Psychological Treatment (PBPT). We performed a multiple logistic regression analysis to identify factors associated with burnout. Adjusted odds ratios (AORs) and associated 95% confidence intervals (CIs) were computed. RESULTS: In total, 328 participants completed the questionnaire. The self-reported burnout level (emotional exhaustion>27 and/or depersonalization>10) was 67.4%. The majority of the respondents were female (83.9%), Singaporean (73.5%), aged 40 years and below (84.2%), and Chinese ethnicity (79.9%). In the multiple logistic regression model, high burnout level was negatively associated with being in the age groups of 31 to 40 (AOR 0.39, 95% CI 0.16-0.93) and 40 years and older (AOR 0.30, 95% CI 0.10-0.87) and a low self-reported workload (AOR 0.35, 95% CI 0.23-0.52). High burnout level was positively associated with a work experience of three to five years (AOR 5.27, 95% CI 1.44-20.93) and more than five years (AOR 4.24; 95% CI 1.16-16.79. One hundred and ninety participants completed the PBPT component. The most frequently cited barriers to seeking psychological help by participants with burnout (n = 130) were 'negative evaluation of therapy' and 'time constraints.' CONCLUSIONS: This study shows a high self-reported burnout level and identifies its associated factors among AHPs in a tertiary hospital. The findings revealed the urgency of addressing burnout in AHPs and the need for effective interventions to reduce burnout. Concurrently, proper consideration of the barriers to seeking help is warranted to improve AHPs' mental well-being.

Antenatal sleep quality associated with perinatal outcomes in women of advanced maternal age.

OBJECTIVES: The increasing prevalence of advanced maternal age (AMA) coupled with poor sleep quality among pregnant women makes it important to study their association with perinatal outcomes. However, little is known about the interaction of AMA and maternal antenatal sleep on perinatal outcomes. Here, we examined whether associations between AMA and perinatal outcomes are modified by antenatal sleep quality. PARTICIPANTS: Data were collected from 446 women, with a singleton pregnancy and no pregnancy complications, who participated in the Growing Up in Singapore Towards healthy Outcomes (GUSTO) birth cohort study. MEASUREMENTS: Participants completed the Pittsburgh Sleep Quality Index (PSQI) at 26-28 weeks gestation and had perinatal outcome data collected upon delivery. Interactions between AMA and maternal sleep quality on perinatal outcomes were investigated and where significant, analyses were further stratified by maternal age. All analyses were adjusted for maternal BMI at 26-28 weeks gestation, ethnicity, and maternal education. RESULTS: Neonates of mothers of AMA and poor sleep quality (PSQI score >5) had increased odds of stay in the neonatal intensive care unit (adjusted odds ratio = 3.53, 95% CI: -1.21 to 10.27) and shorter birth length (adjusted mean difference = -1.05 cm, 95% CI: -1.82 to -0.20), as compared with women of AMA and good sleep quality (PSQI score ≤5). In women <35 years, sleep quality did not associate with perinatal outcomes. CONCLUSION: Poor sleep quality in women of AMA was associated with neonatal health outcomes. Improving maternal antenatal sleep may potentially improve perinatal outcomes in offspring of women of AMA.