Recognition of paediatric otopathology by General Practitioners

Buchanan, Carolyn M.; Pothier, David D.

doi:10.1016/j.ijporl.2008.01.030

Cited by 28 publications

(30 citation statements)

References 8 publications

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“…Access to otolaryngology is limited by lengthy wait lists and lack of specialists, especially in rural and remote areas. Initial diagnosis and triage of patients with ear disease often falls on primary care providers who are relatively inaccurate with common diagnoses such as serous otitis media (SOM) and acute otitis media (AOM) . In a multinational study of general practitioners and pediatricians, the average video otoscopic accuracy for SOM and AOM was only 46% and 51%, respectively .…”

Section: Introductionmentioning

confidence: 99%

Otoscopic diagnosis using computer vision: An automated machine learning approach

Livingstone

Chau²

2019

The Laryngoscope

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Objective: Access to otolaryngology is limited by lengthy wait lists and lack of specialists, especially in rural and remote areas. The objective of this study was to use an automated machine learning approach to build a computer vision algorithm for otoscopic diagnosis capable of greater accuracy than trained physicians. This algorithm could be used by primary care providers to facilitate timely referral, triage, and effective treatment.Methods: Otoscopic images were obtained from Google Images (Google Inc., Mountain View, CA), from open access repositories, and within otolaryngology clinics associated with our institution. After preprocessing, 1,366 unique images were uploaded to the Google Cloud Vision AutoML platform (Google Inc.) and annotated with one or more of 14 otologic diagnoses. A consensus set of labels for each otoscopic image was attained, and a multilabel classifier architecture algorithm was trained. The performance of the algorithm on an 89-image test set was compared to the performance of physicians from pediatrics, emergency medicine, otolaryngology, and family medicine.Results: For all diagnoses combined, the average precision (positive predictive value) of the algorithm was 90.9%, and the average recall (sensitivity) was 86.1%. The algorithm made 79 correct diagnoses with an accuracy of 88.7%. The average physician accuracy was 58.9%.Conclusion: We have created a computer vision algorithm using automated machine learning that on average rivals the accuracy of the physicians we tested. Fourteen different otologic diagnoses were analyzed. The field of medicine will be changed dramatically by artificial intelligence within the next few decades, and physicians of all specialties must be prepared to guide that process.

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Section: Introductionmentioning

confidence: 99%

Otoscopic diagnosis using computer vision: An automated machine learning approach

Livingstone

Chau²

2019

The Laryngoscope

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“…Although middle ear effusion is present in both cases, this clinical classification is important because antibiotics are generally beneficial only for AOM [1, 2]. However, proper diagnosis of AOM as well as distinction from both OME and no effusion (NOE) requires considerable training (see Figure 1, e.g., images).…”

Section: Introductionmentioning

confidence: 99%

Automated Diagnosis of Otitis Media: Vocabulary and Grammar

Kuruvilla

Shaikh

Hoberman

et al. 2013

International Journal of Biomedical Imaging

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We propose a novel automated algorithm for classifying diagnostic categories of otitis media: acute otitis media, otitis media with effusion, and no effusion. Acute otitis media represents a bacterial superinfection of the middle ear fluid, while otitis media with effusion represents a sterile effusion that tends to subside spontaneously. Diagnosing children with acute otitis media is difficult, often leading to overprescription of antibiotics as they are beneficial only for children with acute otitis media. This underscores the need for an accurate and automated diagnostic algorithm. To that end, we design a feature set understood by both otoscopists and engineers based on the actual visual cues used by otoscopists; we term this the otitis media vocabulary. We also design a process to combine the vocabulary terms based on the decision process used by otoscopists; we term this the otitis media grammar. The algorithm achieves 89.9% classification accuracy, outperforming both clinicians who did not receive special training and state-of-the-art classifiers.

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“…En Atención Primaria se tiende a sobrestimar la OMA y a subestimar la OMS 24 . Entre los médicos generales solo se diagnosticó la OM por otoscopia simple en un 53% de pacientes, concluyéndose que, si se enseñara a limpiar la cera del conducto y a utilizar la otoscopia neumática, mejoraría esta valoración 23,25 . Los pediatras tuvieron un 20-25% menos de aciertos otoscópicos que los ORL 26 , pero, a pesar de ello, los que utilizaron regularmente la ON prescribieron globalmente menos antibióticos 2 .…”

Section: Discussionunclassified

Otoscopia neumática

Orejas

Vicente

Jorge³

et al. 2015

Rev Pediatr Aten Primaria

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Tras explicar en un artículo previo la mejor forma de limpiar la cera del oído, pretendemos con este segundo trabajo mostrar las ventajas que tiene la otoscopia neumática (ON) en el diagnóstico de la otitis media (OM), exponiendo el procedimiento y los hallazgos exploratorios. Las conclusiones aportadas conjugan, nuevamente, las recomendaciones ofrecidas por la revisión bibliográfica efectuada en relación a la otoscopia neumática, con la experiencia de un equipo de pediatras y de otorrinolaringólogos de la misma área de salud. Las guías americanas y la mayoría de pediatras consideran que la ON es el método preferido para examinar en los niños la presencia de exudado de oído medio, sin embargo, muy pocos lo usan. Es un procedimiento sencillo, económico, fehaciente y fácilmente accesible en Atención Primaria; pero siendo la valoración subjetiva, es necesario un entrenamiento previo dirigido. Consideramos fundamental el aprendizaje y la aplicación de la otoscopia neumática en Atención Primaria, como procedimiento diagnóstico del derrame de oído medio. After explaining the best way to clean ear wax in a previous article, in the second one we aim to show the advantages of pneumatic otoscopy (PO) when diagnosing otitis media (OM), exposing the procedure and exploratory findings. Our conclusions sum up again the recommendations offered by the literature review carried out on pneumatic otoscopy, and the experience of a team of pediatricians and otolaryngologists from the same Healthcare Area. American guidelines and most pediatricians consider PO is the best method to examine the middle ear in children when effusion is present; however, very few pediatricians use it. It is a simple, economical, reliable and easily accessible procedure in Primary care; but, as it is a subjective technique, a prior targeted training is necessary. We consider that learning and using the pneumatic otoscopy is essential in Primary care, as a diagnostic procedure in middle ear effusion.

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Recognition of paediatric otopathology by General Practitioners

Cited by 28 publications

References 8 publications

Otoscopic diagnosis using computer vision: An automated machine learning approach

Otoscopic diagnosis using computer vision: An automated machine learning approach

Automated Diagnosis of Otitis Media: Vocabulary and Grammar

Otoscopia neumática

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