scholarly journals New Approaches and Technologies to Improve Accuracy of Acute Otitis Media Diagnosis

Diagnostics ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2392
Author(s):  
Susanna Esposito ◽  
Sonia Bianchini ◽  
Alberto Argentiero ◽  
Riccardo Gobbi ◽  
Claudio Vicini ◽  
...  

Several studies have shown that in recent years incidence of acute otitis media (AOM) has declined worldwide. However, related medical, social, and economic problems for patients, their families, and society remain very high. Better knowledge of potential risk factors for AOM development and more effective preventive interventions, particularly in AOM-prone children, can further reduce disease incidence. However, a more accurate AOM diagnosis seems essential to achieve this goal. Diagnostic uncertainty is common, and to avoid risks related to a disease caused mainly by bacteria, several children without AOM are treated with antibiotics and followed as true AOM cases. The main objective of this manuscript is to discuss the most common difficulties that presently limit accurate AOM diagnosis and the new approaches and technologies that have been proposed to improve disease detection. We showed that misdiagnosis can be dangerous or lead to relevant therapeutic mistakes. The need to improve AOM diagnosis has allowed the identification of a long list of technologies to visualize and evaluate the tympanic membrane and to assess middle-ear effusion. Most of the new instruments, including light field otoscopy, optical coherence tomography, low-coherence interferometry, and Raman spectroscopy, are far from being introduced in clinical practice. Video-otoscopy can be effective, especially when it is used in association with telemedicine, parents’ cooperation, and artificial intelligence. Introduction of otologic telemedicine and use of artificial intelligence among pediatricians and ENT specialists must be strongly promoted in order to reduce mistakes in AOM diagnosis.

1998 ◽  
Vol 107 (2) ◽  
pp. 120-128 ◽  
Author(s):  
Ramsey Alsarraf ◽  
Chung J. Jung ◽  
Catherine Crowley ◽  
Jonathan Perkins ◽  
George A. Gates

There are no reliable and valid instruments that measure otitis media clinical or functional health status in children ages 1 to 3 years. This study develops and tests three new instruments of clinical and functional otitis health status: the Otitis Media Clinical Severity Index (OM-CSI), the Otitis Media Functional Status Questionnaire (OM-FSQ), and the Otitis Media Diary (OMD). The OM-CSI was found to be a reliable measure of clinical acute otitis media (AOM) severity, with high internal consistency (Cronbach's α) scores, as well as an accurate indicator of AOM severity. The OM-FSQ and OMD were demonstrated to be reliable and valid measures of otitis-specific functional health status, with reproducible scores over time, high internal consistency α scores, and high correlation with measures of AOM clinical severity and other functional health status instruments. These three new instruments were also sensitive and specific indicators of AOM episodes.


2012 ◽  
Vol 126 (10) ◽  
pp. 976-983 ◽  
Author(s):  
Edward C Toll ◽  
Desmond A Nunez

AbstractBackground:Acute otitis media is very common, but diagnostic criteria and treatment recommendations vary considerably.Methods:Medline, the Cochrane Database of Systematic Reviews and the Cochrane Central Register of Controlled Trials were searched using the key words ‘acute otitis media’ AND ‘diagnosis’ OR ‘diagnostic criteria’ OR ‘definition’, and by combining the terms ‘acute otitis media’ AND ‘guidelines’. PubMed was searched using the key words ‘mastoiditis’ and ‘prevalence’.Results:The 11 most recently published guidelines unanimously agreed that adequate analgesia should be prescribed in all cases. The majority recommended that routine antibiotic prescription should be avoided in mild to moderate cases and when there was diagnostic uncertainty in patients two years and older. Antibiotics were recommended in children two years and younger, most commonly a 5-day course of amoxicillin (or a macrolide in patients allergic to penicillin).Conclusion:Level 1A evidence shows that selected cases of acute otitis media benefit from antibiotic prescription.


Author(s):  
Al-Rahim Habib ◽  
Majid Kajbafzadeh ◽  
Zubair Hasan ◽  
Eugene Wong ◽  
Hasantha Gunasekera ◽  
...  

Objective: To summarize the accuracy of artificial intelligence (AI) computer vision algorithms to classify ear disease from otoscopy. Methods: Using the PRISMA guidelines, nine online databases were searched for articles that used AI methods (convolutional neural networks, artificial neural networks, support vector machines, decision trees, k-nearest neighbors) to classify otoscopic images. Diagnostic classes of interest: normal tympanic membrane, acute otitis media (AOM), otitis media with effusion (OME), chronic otitis media (COM) with or without perforation, cholesteatoma, and canal obstruction. Main Outcome Measures: Accuracy to correctly classify otoscopic images compared to otolaryngologists (ground-truth). The Quality Assessment of Diagnostic Accuracy Studies Version 2 tool was used to assess the quality of methodology and risk of bias. Results: Thirty-nine articles were included. Algorithms achieved 90.7% (95%CI: 90.1 – 91.3%) accuracy to difference between normal or abnormal otoscopy images in 14 studies. The most common multi-classification algorithm (3 or more diagnostic classes) achieved 97.6% (95%CI: 97.3.- 97.9%) accuracy to differentiate between normal, AOM and OME in 3 studies. Compared to manual classification, AI algorithms outperformed human assessors to classify otoscopy images achieving 93.4% (95%CI: 90.5 – 96.4%) versus 73.2% (95%CI: 67.9 – 78.5%) accuracy in 3 studies. Convolutional neural networks achieved the highest accuracy compared to other classification methods. Conclusion: AI can classify ear disease from otoscopy. A concerted effort is required to establish a comprehensive and reliable otoscopy database for algorithm training. An AI-supported otoscopy system may assist health care workers, trainees, and primary care practitioners with less otology experience identify ear disease.


JAMA ◽  
1966 ◽  
Vol 197 (11) ◽  
pp. 849-853 ◽  
Author(s):  
O. F. Roddey

1994 ◽  
Vol 110 (1) ◽  
pp. 115-121 ◽  
Author(s):  
P ANTONELLI ◽  
S JUHN ◽  
C LE ◽  
G GIEBINK

2011 ◽  
Vol 41 (10) ◽  
pp. 26
Author(s):  
MICHAEL E. PICHICHERO

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