Otitis media is a general term for middle-ear inflammation that is classified clinically as either acute otitis media (AOM) or otitis media with effusion (OME). AOM represents a bacterial super infection of the middle ear fluid and OME a sterile effusion that tends to subside spontaneously. Antibiotics are generally beneficial only for AOM. Accurate diagnosis of AOM, as well as distinction from both OME and no effusion (NOE) requires considerable training. AOM is the most common infection for which antimicrobial agents are prescribed for children in the US. By age seven, 93 percent of children will have experienced one or more episodes of otitis media.1 AOM results in significant social burden and indirect costs due to time lost from school and work. Estimated direct costs of AOM in 1995 were $1.96 billion and indirect costs were estimated to be $1.02 billion, with a total of 20 million prescriptions for antimicrobials related to otitis media.2 Given these considerations, our goal is to: Develop a software tool to classify images into one of three stringent clinical diagnostic categories (AOM/OME/NOE), and validate the algorithm on tympanic membrane (TM) images. We have assembled a strong multidisciplinary team that can successfully develop an automated diagnostic algorithm in this Phase-I program. We have (1) gathered a team of nationally-recognized otoscopists with substantial clinical and research experience in the context of AOM clinical trials; (2) studied the predictive value of diagnostic findings in discriminating AOM from OME from NOE; (3) acquired a large number of TM images from children; and (4) involved an internationally recognized expert in developing algorithms in all areas of image analysis and processing. In the planned Phase-II, we will use the algorithm developed in the Phase-I program and incorporate it into a user-friendly and marketable digital otoscope-software platform that can be used at the point-of-care by clinicians to improve the care of children with this frequently occurring condition. This will be followed by a clinical trial evaluating its immediate impact on clinical care, and, in particular, utilization of antimicrobials. Our main goal will be to develop an accurate automated algorithm for classifying the three diagnostic categories (AOM/OME/NOE). We aim to achieve an overall accuracy of 95 percent by applying a newly developed classification algorithm. This will include applying state-of-the-art classification methods as well as segmentation algorithms, for automated, robust diagnosis and classification of the three diagnostic categories (AOM/OME/NOE). We propose to achieve this through the following two specific aims: Specific Aim 1: Develop a robust and accurate diagnostic algorithm that can discriminate TM digital images into 1of 3 stringent diagnostic categories (AOM/OME/NOE). Specific Aim 2: Validate the algorithm on a dataset that includes over 2000 TM images collected in a recently completed NIAID-sponsored clinical trial.
Public Health Relevance: AOM is the most common infection for which antimicrobial agents are prescribed in children in the US. By age seven, 93 percent of children will have experienced one or more episodes of otitis media. AOM results in significant social burden and indirect costs due to time lost from school and work. Estimated direct costs of AOM in 1995 were $1.96 billion and indirect costs were estimated to be $1.02 billion, with a total of 20 million prescriptions for antimicrobials related to otitis media. Developing an automated and accurate software tool to help classify otitis media images into one of three stringent clinical categories would have a great impact on both clinical care as well as reducing the unnecessary prescriptions of antibiotics in the US.
Thesaurus Terms: 0-11 Years Old; Accuracy Of Diagnosis; Acute; Age; Algorithms; Antibiotic Agents; Antibiotic Drugs; Antibiotics; Area; Arts; Bacteria Resistance; Bacteria Resistant; Bacterial Resistant; Categories; Child; Child Care; Child Youth; Children (0-21); Classification; Clinical; Clinical Research; Clinical Study; Clinical Trials; Clinical Trials, Unspecified; Computer Programs; Computer Software Tools; Computer Software; Consensus; Data Set; Dataset; Diagnosis; Diagnostic; Diagnostic Findings; Direct Costs; Eff; Ear; Ear Structure; Eardrum; F And A; Facilities And Administrative Costs; Facilities And Administrative Costs (F And A); Goals; Human, Child; Inflm; Image; Image Analyses; Image Analysis; Indirect Costs; Infection; Inflammation; Learning, Machine; Liquid Substance; Machine Learning; Membrana Tympanica; Methods; Methods And Techniques; Methods, Other; Middle Ear Effusion; Miscellaneous Antibiotic; Otitis Media; Otitis Media With Effusion; Otitis Media, Secretory; Otoscopes; Phase; Physicians; Predictive Value; Process; Programs (Pt); Programs [publication Type]; Puericulture; Roc Analysis; Schools; Signs And Symptoms; Site; Software; Software Tools; Solutions; Sterility; Superinfection; Superinvasion, Microbial; System; System, Loinc Axis 4; Systematics; Techniques; Time; Tools, Software; Training; Tympanic Membrane; Tympanic Membrane Structure; Work; Anti-Microbial; Anti-Microbial Agent; Anti-Microbial Drug; Antimicrobial; Antimicrobial Agent; Antimicrobial Drug; Bacterial Resistance; Children; Clinical Care; Clinical Investigation; Computer Imaging; Computer Program/Software; Diagnostic Accuracy; Digital; Digital Imaging; Ear Drum; Ear Infection; Effusion; Experience; Fluid; Image Evaluation; Image Processing; Imaging; Improved; Kernel Methods; Liquid; Middle Ear; Multidisciplinary; Overgrowth Bacterial; Point Of Care; Programs; Public Health Relevance; Resistance To Bacteria; Resistance To Bacterial; Resistant To Bacteria; Resistant To Bacterial; Skills; Social; Statistical Learning; Sterile; Support Vector Machine; Tool; User-Friendly; Youngster
