By Tarun Sai LomteMar 6 2024Reviewed by Lily Ramsey, LLM In a recent study published in JAMA Pediatrics, researchers developed and validated an automated classifier for diagnosing acute otitis media in children.
Several studies have leveraged deep learning for training neural networks to detect AOM and other ear-related conditions, albeit with limited clinical application. An endoscope or otoscope connected to the smartphone was used to capture videos of children’s TMs. Two otoscopists reviewed the videos and assigned a diagnosis.
Additionally, a decision tree model was developed as an alternative to examine if the results would be different; this used DR-RNN model-predicted TM features. A receiver operating characteristic curve was generated for the DR-RNN model by plotting true and false positive results at different probability thresholds. ROC was not plotted for the DT model as it was not probabilistic.
The corresponding figures for the DT model were 93.7% and 93.3% respectively. For the DR-RNN model, the area under the ROC was 0.973.
Source: Tech Daily Report (techdailyreport.net)
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