Creating a histology-embryology free digital image database using high-end microscopy and computer techniques for on-line biomedical education

Evaluation of the usage of YouTube videos about Histology and Embryology as an educational material

The use of YouTube videos for educational purposes has been increasingly popular. The quality and accuracy of the information level of these videos should be checked by expert trainers. This study aims to evaluate the content, quality and functionality of YouTube videos on Histology and Embryology and to measure their educational usefulness. In the study, searches were performed using the keywords "Histology" and "Embryology" in the YouTube search tab. Quality and content were evaluated using the Video Power Index (VPI), modified DISCERN scale, JAMA and the Global Quality Scale (GQS). Videos were categorized by educational usefulness. SPSS software was used for statistical analysis. A statistically significant high correlation was observed between modified DISCERN scores and JAMA scores (r = 0.757, p < 0.001) and between modified DISCERN scores and GQS scores (r = 0.743, p < 0.001). A statistically significant high correlation was also determined between JAMA and GQS scores (r = 0.632, p < 0.001). GQS scores were weakly, negatively and significantly correlated with the number of comments (r = -0.302, p < 0.05) and dislikes (r = -0.325, p < 0.05). Based on GQS, the useful and non-useful videos differed significantly in terms of views, likes, dislikes, comments counts and days since upload (p < 0.05). Modified DISCERN and JAMA scores also differed significantly between the useful and non-useful videos (p < 0.001). Educational videos published for Histology and Embryology education on the internet will be more beneficial if they are prepared by expert educators from reliable information sources, by the current literature, and by scoring systems such as DISCERN, JAMA and GQS.

Performance of Dental Students in Understanding and Retention of Oral Pathology Concepts: A Comparative Analysis of Traditional versus Live-Field Teaching Methods

Background

Understanding oral aspects of pathology by traditional techniques has always been a paradigm in the field of dental education. Traditional methods of teaching include interaction using black board, projectors, and alternate methods of teaching such as a student-centered approach where live-field demonstrations, audio visual aids, and student interaction are also gaining importance, ultimately promoting active education. The aim of the study was to compare live-field and static-field teaching methods in understanding and retention of the histopathological features in dental students.

Methods

This was a cross-sectional analytical study, wherein a uniform cohort of III-year dental students was obtained by randomizing the study subjects. Practical classes were conducted using traditional black board/static pictures and dynamic live-field teaching comprising of microscope connected to an HD screen and projector demonstrating the preferred microscopic field. Alternately, the level of retention of knowledge was measured using customized topic-based tests. The comparison of average scores was done between live-field and static-field teaching groups using the paired t-test.

Results

The test scores using the paired t-test were marginally elevated in the conventional mode of teaching; however, it varied with respect to precise topics taken using both the genres of teaching.

Conclusion

A balance of both conventional and virtual teaching needs to be achieved to enhance the comprehension in student learning. Nevertheless, in the impending years, advanced research is entailed to see if the virtual mode of teaching could replace the conventional method for the advancement in the study prospects.

Effects of Image Quantity and Image Source Variation on Machine Learning Histology Differential Diagnosis Models

Aims

Histology, the microscopic study of normal tissues, is a crucial element of most medical curricula. Learning tools focused on histology are very important to learners who seek diagnostic competency within this important diagnostic arena. Recent developments in machine learning (ML) suggest that certain ML tools may be able to benefit this histology learning platform. Here, we aim to explore how one such tool based on a convolutional neural network, can be used to build a generalizable multi-classification model capable of classifying microscopic images of human tissue samples with the ultimate goal of providing a differential diagnosis (a list of look-alikes) for each entity.

Methods

We obtained three institutional training datasets and one generalizability test dataset, each containing images of histologic tissues in 38 categories. Models were trained on data from single institutions, low quantity combinations of multiple institutions, and high quantity combinations of multiple institutions. Models were tested against withheld validation data, external institutional data, and generalizability test images obtained from Google image search. Performance was measured with macro and micro accuracy, sensitivity, specificity, and f1-score.

Results

In this study, we were able to show that such a model's generalizability is dependent on both the training data source variety and the total number of training images used. Models which were trained on 760 images from only a single institution performed well on withheld internal data but poorly on external data (lower generalizability). Increasing data source diversity improved generalizability, even when decreasing data quantity: models trained on 684 images, but from three sources improved generalization accuracy between 4.05% and 18.59%. Maintaining this diversity and increasing the quantity of training images to 2280 further improved generalization accuracy between 16.51% and 32.79%.

Conclusions

This pilot study highlights the significance of data diversity within such studies. As expected, optimal models are those that incorporate both diversity and quantity into their platforms.s.

Students' Views on Difficulties in Learning Histology

The aim of this study was to provide a better understanding of the main difficulties hindering undergraduate biology students in learning histology. The study utilized a self-administered questionnaire which included three closed-ended and two open-ended questions: (1) if students had difficulty in learning about each tissue type; (2) what might be the problem in learning about the tissue at hand; (3) which topics were the most difficult; (4) what were the possible reasons that made image identification of tissue types difficult; and (5) how to improve the course curriculum from a student perspective. The survey was administered to 139 undergraduate biology students enrolled in a histology course, of which 101 surveys were completed and analyzed both qualitatively and quantitatively. The topics that students experienced the most difficulties with were: nervous tissue, plant tissues, bone tissues, and glandular epithelial tissue. The main reasons students experienced difficulties with these tissue types, according to the students themselves, were the nature of the topic, grasping the terminology used, and insufficient teaching time. Students suggested the adoption of strategies such as: teaching based on practical tasks; reducing the content of the histology curriculum; adding anatomy subjects; and making histology education more interesting.

CellFinder: a cell data repository

CellFinder (http://www.cellfinder.org) is a comprehensive one-stop resource for molecular data characterizing mammalian cells in different tissues and in different development stages. It is built from carefully selected data sets stemming from other curated databases and the biomedical literature. To date, CellFinder describes 3394 cell types and 50 951 cell lines. The database currently contains 3055 microscopic and anatomical images, 205 whole-genome expression profiles of 194 cell/tissue types from RNA-seq and microarrays and 553 905 protein expressions for 535 cells/tissues. Text mining of a corpus of >2000 publications followed by manual curation confirmed expression information on ∼900 proteins and genes. CellFinder’s data model is capable to seamlessly represent entities from single cells to the organ level, to incorporate mappings between homologous entities in different species and to describe processes of cell development and differentiation. Its ontological backbone currently consists of 204 741 ontology terms incorporated from 10 different ontologies unified under the novel CELDA ontology. CellFinder’s web portal allows searching, browsing and comparing the stored data, interactive construction of developmental trees and navigating the partonomic hierarchy of cells and tissues through a unique body browser designed for life scientists and clinicians.

E-learning in medical education in resource constrained low- and middle-income countries

BACKGROUND

In the face of severe faculty shortages in resource-constrained countries, medical schools look to e-learning for improved access to medical education. This paper summarizes the literature on e-learning in low- and middle-income countries (LMIC), and presents the spectrum of tools and strategies used.

METHODS

Researchers reviewed literature using terms related to e-learning and pre-service education of health professionals in LMIC. Search terms were connected using the Boolean Operators "AND" and "OR" to capture all relevant article suggestions. Using standard decision criteria, reviewers narrowed the article suggestions to a final 124 relevant articles.

RESULTS

Of the relevant articles found, most referred to e-learning in Brazil (14 articles), India (14), Egypt (10) and South Africa (10). While e-learning has been used by a variety of health workers in LMICs, the majority (58%) reported on physician training, while 24% focused on nursing, pharmacy and dentistry training. Although reasons for investing in e-learning varied, expanded access to education was at the core of e-learning implementation which included providing supplementary tools to support faculty in their teaching, expanding the pool of faculty by connecting to partner and/or community teaching sites, and sharing of digital resources for use by students. E-learning in medical education takes many forms. Blended learning approaches were the most common methodology presented (49 articles) of which computer-assisted learning (CAL) comprised the majority (45 articles). Other approaches included simulations and the use of multimedia software (20 articles), web-based learning (14 articles), and eTutor/eMentor programs (3 articles). Of the 69 articles that evaluated the effectiveness of e-learning tools, 35 studies compared outcomes between e-learning and other approaches, while 34 studies qualitatively analyzed student and faculty attitudes toward e-learning modalities.

CONCLUSIONS

E-learning in medical education is a means to an end, rather than the end in itself. Utilizing e-learning can result in greater educational opportunities for students while simultaneously enhancing faculty effectiveness and efficiency. However, this potential of e-learning assumes a certain level of institutional readiness in human and infrastructural resources that is not always present in LMICs. Institutional readiness for e-learning adoption ensures the alignment of new tools to the educational and economic context.

Virtual microscopy in a veterinary curriculum

Teaching faculty in the University of Tennessee College of Veterinary Medicine assist students in their professional education by providing a new way of viewing microscopic slides digitally. Faculty who teach classes in which glass slides are used participate in a program called Virtual Microscopy. Glass slides are digitized using a state-of-the-art integrated system, and a personal computer functions as the "microscope." Additionally, distribution of the interactive images is enhanced because they are available to students online. The digital slide offers equivalent quality and resolution to the original glass slide viewed on a microscope and has several additional advantages over microscopes. Students can choose to examine the entire slide at any of several objectives; they are able to access the slides (called WebSlides) from the college's server, using either Internet Explorer or a special browser developed by Bacus Laboratories, Inc.,(a) called the WebSlide browser, which lets the student simultaneously view a low-objective image and one or two high-objective images of the same slide. The student can "move the slide" by clicking and dragging the image to a new location. Easy archiving, annotation of images, and Web conferencing are additional features of the system.