DESIGN: Artificial intelligence (neural network) study.
METHODS: We assessed 1400 OCT scans of patients with neovascular AMD. Fifteen physical features for each eligible OCT, as well as patient age, were used as input data and corresponding recorded visual acuity as the target data to train, validate, and test a supervised neural network. We then applied this network to model the impact on acuity of defined OCT changes in subretinal fluid, subretinal hyperreflective material, and loss of external limiting membrane (ELM) integrity.
RESULTS: A total of 1210 eligible OCT scans were analyzed, resulting in 1210 data points, which were each 16-dimensional. A 10-layer feed-forward neural network with 1 hidden layer of 10 neurons was trained to predict acuity and demonstrated a root mean square error of 8.2 letters for predicted compared to actual visual acuity and a mean regression coefficient of 0.85. A virtual model using this network demonstrated the relationship of visual acuity to specific, programmed changes in OCT characteristics. When ELM is intact, there is a shallow decline in acuity with increasing subretinal fluid but a much steeper decline with equivalent increasing subretinal hyperreflective material. When ELM is not intact, all visual acuities are reduced. Increasing subretinal hyperreflective material or subretinal fluid in this circumstance reduces vision further still, but with a smaller gradient than when ELM is intact.
CONCLUSIONS: The supervised machine learning neural network developed is able to generate an estimated visual acuity value from OCT images in a population of patients with AMD. These findings should be of clinical and research interest in macular degeneration, for example in estimating visual prognosis or highlighting the importance of developing treatments targeting more visually destructive pathologies.
METHODS: Self-administered questionnaires were distributed to 233 undergraduate dental students involved with clinical teaching. This modified and validated questionnaire focusing on students' learning environment was used in order to gain relevant information related to dental clinical teaching. Six domains with different criteria applicable to clinical teaching in dentistry were selected consisting of modelling (four criteria), coaching (four criteria), scaffolding (four criteria), articulation (four criteria), reflection (two criteria) and general learning environment (six criteria). Data analyses were performed using IBM SPSS Statistics 20.
RESULTS: Majority of the students expressed positive perceptions on their clinical learning experience towards the clinical teachers in the Faculty of Dentistry, University of Malaya, in all criteria of the domains. Few negative feedbacks concerning the general learning environment were reported.
CONCLUSION: Further improvement in the delivery of clinical teaching preferably by using wide variety of teaching-learning activities can be taken into account through students' feedback on their learning experience.