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Efficacy of smartphone-based Mobile learning versus lecture-based learning for instruction of Cephalometric landmark identification.

ABSTRACT: BACKGROUND:Considering the increasing popularity of electronic learning, particularly smartphone-based mobile learning, and its reportedly optimal efficacy for instruction of complicated topics, this study aimed to compare the efficacy of smartphone-based mobile learning versus lecture-based learning for instruction of cephalometric landmark identification. METHODS:This quasi-experimental interventional study evaluated 53 dental students (4th year) in two groups of intervention (n?=?27; smartphone instruction using an application) and control (n?=?26, traditional lecture-based instruction). Two weeks after the instructions, dental students were asked to identify four landmarks namely the posterior nasal spine (PNS), orbitale (Or), articulare (Ar) and gonion (Go) on lateral cephalograms. The mean coordinates of each landmark identified by orthodontists served as the reference point, and the mean distance from each identified point to the reference point was reported as the mean consistency while the standard deviation of this mean was reported as the precision of measurement. Data were analyzed using SPSS version 18 via independent sample t-test. RESULTS:No significant difference was noted between the two groups in identification of PNS, Ar or Go (P?>?0.05). However, the mean error rate in identification of Or was significantly lower in smartphone group compared with the traditional learning group (P?=?0.020). CONCLUSIONS:Smartphone-based mobile learning had a comparable, and even slightly superior, efficacy to lecture-based learning for instruction of cephalometric landmark identification, and may be considered, at least as an adjunct, to enhance the instruction of complicated topics. TRIAL REGISTRATION NUMBER:This is not a human subject research. https://ethics. RESEARCH:ac.ir/ProposalCertificateEn.php?id=33714&Print=true&NoPrintHeader=true&NoPrintFooter=true&NoPrintPageBorder=true&LetterPrint=true .

SUBMITTER: Golshah A

PROVIDER: S-EPMC7457473 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Efficacy of smartphone-based Mobile learning versus lecture-based learning for instruction of Cephalometric landmark identification.

Golshah Amin A Dehdar Fatemeh F Imani Mohammad Moslem MM Nikkerdar Nafiseh N

BMC medical education 20200831 1

<h4>Background</h4>Considering the increasing popularity of electronic learning, particularly smartphone-based mobile learning, and its reportedly optimal efficacy for instruction of complicated topics, this study aimed to compare the efficacy of smartphone-based mobile learning versus lecture-based learning for instruction of cephalometric landmark identification.<h4>Methods</h4>This quasi-experimental interventional study evaluated 53 dental students (4th year) in two groups of intervention (n ...[more]

PMID: 32867758

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Project description:ObjectivesDeep learning (DL) has been increasingly employed for automated landmark detection, e.g., for cephalometric purposes. We performed a systematic review and meta-analysis to assess the accuracy and underlying evidence for DL for cephalometric landmark detection on 2-D and 3-D radiographs.MethodsDiagnostic accuracy studies published in 2015-2020 in Medline/Embase/IEEE/arXiv and employing DL for cephalometric landmark detection were identified and extracted by two independent reviewers. Random-effects meta-analysis, subgroup, and meta-regression were performed, and study quality was assessed using QUADAS-2. The review was registered (PROSPERO no. 227498).DataFrom 321 identified records, 19 studies (published 2017-2020), all employing convolutional neural networks, mainly on 2-D lateral radiographs (n=15), using data from publicly available datasets (n=12) and testing the detection of a mean of 30 (SD: 25; range.: 7-93) landmarks, were included. The reference test was established by two experts (n=11), 1 expert (n=4), 3 experts (n=3), and a set of annotators (n=1). Risk of bias was high, and applicability concerns were detected for most studies, mainly regarding the data selection and reference test conduct. Landmark prediction error centered around a 2-mm error threshold (mean; 95% confidence interval: (-0.581; 95 CI: -1.264 to 0.102 mm)). The proportion of landmarks detected within this 2-mm threshold was 0.799 (0.770 to 0.824).ConclusionsDL shows relatively high accuracy for detecting landmarks on cephalometric imagery. The overall body of evidence is consistent but suffers from high risk of bias. Demonstrating robustness and generalizability of DL for landmark detection is needed.Clinical significanceExisting DL models show consistent and largely high accuracy for automated detection of cephalometric landmarks. The majority of studies so far focused on 2-D imagery; data on 3-D imagery are sparse, but promising. Future studies should focus on demonstrating generalizability, robustness, and clinical usefulness of DL for this objective.

Project description:Simulation-based teaching (SIM) is a common method for medical education. SIM exposes residents to uncommon scenarios that require critical, timely actions. SIM may be a valuable training method for critically ill poisoned patients whose diagnosis and treatment depend on key clinical findings. Our objective was to compare medical simulation (SIM) to traditional lecture-based instruction (LEC) for training emergency medicine (EM) residents in the acute management of critically ill poisoned patients. EM residents completed two pre-intervention questionnaires: (1) a 24-item multiple-choice test of four toxicological emergencies and (2) a questionnaire using a five-point Likert scale to rate the residents' comfort level in diagnosing and treating patients with specific toxicological emergencies. After completing the pre-intervention questionnaires, residents were randomized to SIM or LEC instruction. Two toxicologists and three EM physicians presented four toxicology topics to both groups in four 20-min sessions. One group was in the simulation center, and the other in a lecture hall. Each group then repeated the multiple-choice test and questionnaire immediately after instruction and again at 3 months after training. Answers were not discussed. The primary outcome was comparison of immediate mean post-intervention test scores and final scores 3 months later between SIM and LEC groups. Test score outcomes between groups were compared at each time point (pre-test, post-instruction, 3-month follow-up) using Wilcoxon rank sum test. Data were summarized by descriptive statistics. Continuous variables were characterized by means (SD) and tested using t tests or Wilcoxon rank sum. Categorical variables were summarized by frequencies (%) and compared between training groups with chi-square or Fisher's exact test. Thirty-two EM residents completed pre- and post-intervention tests and comfort questionnaires on the study day. Both groups had higher post-intervention mean test scores (p < 0.001), but the LEC group showed a greater improvement compared to the SIM group (5.6 [2.3] points vs. 3.6 [2.4], p = 0.02). At the 3-month follow-up, 24 (75 %) tests and questionnaires were completed. There was no improvement in 3-month mean test scores in either group compared to immediate post-test scores. The SIM group had higher final mean test scores than the LEC group (16.6 [3.1] vs. 13.3 [2.2], p = 0.009). SIM and LEC groups reported similar diagnosis and treatment comfort level scores at baseline and improved equally after instruction. At 3 months, there was no difference between groups in comfort level scores for diagnosis or treatment. Lecture-based teaching was more effective than simulation-based instruction immediately after intervention. At 3 months, the SIM group showed greater retention than the LEC group. Resident comfort levels for diagnosis and treatment were similar regardless of the type of education.

Dataset Information

Efficacy of smartphone-based Mobile learning versus lecture-based learning for instruction of Cephalometric landmark identification.

Publications

Efficacy of smartphone-based Mobile learning versus lecture-based learning for instruction of Cephalometric landmark identification.

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