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Background: Deep learning (DL)-based artificial intelligence may have different diagnostic characteristics than human experts in medical diagnosis. As a data-driven knowledge system, heterogeneous population incidence in the clinical world is considered to cause more bias to DL than clinicians. Conversely, by experiencing limited numbers of cases, human experts may exhibit large interindividual variability. Thus, understanding how the 2 groups classify given data differently is an essential step for the cooperative usage of DL in clinical application.
Objective: This study aimed to evaluate and compare the differential effects of clinical experience in otoendoscopic image diagnosis in both computers and physicians exemplified by the class imbalance problem and guide clinicians when utilizing decision support systems.
Methods: We used digital otoendoscopic images of patients who visited the outpatient clinic in the Department of Otorhinolaryngology at Severance Hospital, Seoul, South Korea, from January 2013 to June 2019, for a total of 22,707 otoendoscopic images. We excluded similar images, and 7500 otoendoscopic images were selected for labeling. We built a DL-based image classification model to classify the given image into 6 disease categories. Two test sets of 300 images were populated: balanced and imbalanced test sets. We included 14 clinicians (otolaryngologists and nonotolaryngology specialists including general practitioners) and 13 DL-based models. We used accuracy (overall and per-class) and kappa statistics to compare the results of individual physicians and the ML models.
Results: Our ML models had consistently high accuracies (balanced test set: mean 77.14%, SD 1.83%; imbalanced test set: mean 82.03%, SD 3.06%), equivalent to those of otolaryngologists (balanced: mean 71.17%, SD 3.37%; imbalanced: mean 72.84%, SD 6.41%) and far better than those of nonotolaryngologists (balanced: mean 45.63%, SD 7.89%; imbalanced: mean 44.08%, SD 15.83%). However, ML models suffered from class imbalance problems (balanced test set: mean 77.14%, SD 1.83%; imbalanced test set: mean 82.03%, SD 3.06%). This was mitigated by data augmentation, particularly for low incidence classes, but rare disease classes still had low per-class accuracies. Human physicians, despite being less affected by prevalence, showed high interphysician variability (ML models: kappa=0.83, SD 0.02; otolaryngologists: kappa=0.60, SD 0.07).
Conclusions: Even though ML models deliver excellent performance in classifying ear disease, physicians and ML models have their own strengths. ML models have consistent and high accuracy while considering only the given image and show bias toward prevalence, whereas human physicians have varying performance but do not show bias toward prevalence and may also consider extra information that is not images. To deliver the best patient care in the shortage of otolaryngologists, our ML model can serve a cooperative role for clinicians with diverse expertise, as long as it is kept in mind that models consider only images and could be biased toward prevalent diseases even after data augmentation.
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http://dx.doi.org/10.2196/33049 | DOI Listing |
J Health Popul Nutr
December 2024
School of Mathematical Sciences, Universiti Sains Malaysia, Penang, Malaysia.
Background: Foodborne illness is a significant public health concern, particularly in developing countries like Bangladesh. Young adults, aged 18-26 (including undergraduates and recent graduates), are especially vulnerable to the onset of unhealthy eating habits and nutritional imbalances as they begin living independently, often away from their families. This research aims to identify the risk factors associated with the knowledge, attitudes, and practices related to safe food handling among residential university students.
View Article and Find Full Text PDFHealth Res Policy Syst
December 2024
Ecole de Santé Publique, Université Libre de Bruxelles, Brussels, Belgium.
Background: In South Kivu (Eastern Democratic Republic of the Congo [DRC]), health districts (HDs) affected by chronic armed conflicts are devising coping mechanisms to continue offering healthcare services to the population. Nonetheless, this alone does not suffice to make them fully resilient to such conflicts. This study aims to explore the characteristics of these HDs' resilience.
View Article and Find Full Text PDFBMC Oral Health
December 2024
Divisional Hospital, Meeravodai, Sri Lanka.
Background: Dental anxiety has become a major concern for both dental practitioners and patients and prevents a significant proportion of people from attending dental clinics. The present study aimed to determine dental anxiety and associated factors among adult patients attending a public outpatient dental clinic in a base hospital, in the Eastern Province of Sri Lanka.
Methods: A descriptive cross-sectional study was conducted among 400 adults aged 18 to 75 years awaiting dental treatment.
BMC Med Inform Decis Mak
December 2024
Department of Cardiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
Background: Psychological disorders, such as anxiety and depression, are considered to be one of the causes of noncardiac chest pain (NCCP). And these patients can be challenging to differentiate from coronary artery disease (CAD), leading to a considerable number of patients still undergoing angiography. We aim to develop a practical prediction model and nomogram using cardiopulmonary exercise testing (CPET), to help identify these patients.
View Article and Find Full Text PDFSci Rep
December 2024
University of Passau, Chair for Multilingual Computerlinguistics, 94032, Passau, Germany.
We present a novel approach for testing genealogical relations between language families. Our method, which has previously only been applied to closely related languages, makes predictions for cognate reflexes based on the regularity of proposed sound correspondences between language families that are hypothesized to be related. We test the hypothesis about a genealogical relation between Panoan and Takanan, two linguistic families of the Amazon.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!