Interpretation of pediatric chest radiographs by non-radiologist clinicians in Botswana using World Health Organization criteria for endpoint pneumonia

Fawole, Oluwatunmise A.; Kelly, Matthew S.; Steenhoff, Andrew P.; Feemster, Kristen A.; Crotty, Eric J.; Rattan, Mantosh S.; David, Thuso; Mazhani, Tiny; Shah, Samir S.; Andronikou, Savvas; Arscott-Mills, Tonya

doi:10.1007/s00247-020-04625-0

Cited by 9 publications

(5 citation statements)

References 26 publications

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“…Additionally, the technical level of radiologists is also important to make the diagnosis correctly. A study in [4] conducted an education inperson training for improving chest radiograph interpretation accuracy among non-radiologists clinicians.…”

Section: Introductionmentioning

confidence: 99%

Viral and Bacterial Pneumonia Diagnosis via Deep Learning Techniques and Model Explainability

Nguyen¹,

Bao²,

Hoang³

et al. 2020

IJACSA

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Pneumonia is one of the most serious diseases for infants and young children, people older than age 65, and people with health problems or weakened immune systems. From numerous studies, scientists have found that a variety of organisms, including bacteria, viruses, and fungi, can be the cause of the disease. Coronavirus pandemic (COVID-2019) which comes from a type of pneumonia has been causing hundreds of thousands of deaths and is still progressing. Machine learning approaches are applied to develop models for medicine but they still work as a black-box are difficult to interpret output generated by machine learning models. In this study, we propose a method for image-based diagnosis for Pneumonia leveraging deep learning techniques and interpretability of explanation models such as Local Interpretable Model-agnostic Explanations and Saliency maps. We experiment on a variety of sizes and Convolutional neural network architecture to evaluate the efficiency of the proposed method on the set of Chest x-ray images. The work is expected to provide an approach to distinguish between healthy individuals and patients who are affected by Pneumonia as well as differentiate between viral Pneumonia and bacteria Pneumonia by providing signals supporting image-based disease diagnosis approaches.

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Section: Introductionmentioning

confidence: 99%

Viral and Bacterial Pneumonia Diagnosis via Deep Learning Techniques and Model Explainability

Nguyen¹,

Bao²,

Hoang³

et al. 2020

IJACSA

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“…38 Although there is a relative paucity of literature implementing and evaluating performance-based assessments in CPD activities, the evidence to date examining in-person or team-based competency education has been very favorable. [39][40][41][42] Health care facilities, educators, and licensing agencies, are increasingly advocating for the adoption of the CBME approach at the CPD level to ensure that practicing physicians maintain competence throughout their careers, and this study adds evidence to support a CBME approach for practicing physicians. 41,43 Using this framework, clinician accuracy increased on average about 15 to 20% in absolute terms, and often within a relatively brief time commitment that is comparable to attending a lecture at a conference.…”

Section: Discussionmentioning

confidence: 89%

A Performance-Based Competency Assessment of Pediatric Chest Radiograph Interpretation Among Practicing Physicians

Bregman

Thau

Pusic

et al. 2022

J Contin Educ Health Prof

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Introduction: There is limited knowledge on pediatric chest radiograph (pCXR) interpretation skill among practicing physicians. We systematically determined baseline interpretation skill, the number of pCXR cases physicians required complete to achieve a performance benchmark, and which diagnoses posed the greatest diagnostic challenge. Methods: Physicians interpreted 434 pCXR cases via a web-based platform until they achieved a performance benchmark of 85% accuracy, sensitivity, and specificity. Interpretation difficulty scores for each case were derived by applying one-parameter item response theory to participant data. We compared interpretation difficulty scores across diagnostic categories and described the diagnoses of the 30% most difficult-to-interpret cases.Results: 240 physicians who practice in one of three geographic areas interpreted cases, yielding 56,833 pCXR case interpretations. The initial diagnostic performance (first 50 cases) of our participants demonstrated an accuracy of 68.9%, sensitivity of 69.4%, and a specificity of 68.4%. The median number of cases completed to achieve the performance benchmark was 102 (interquartile range 69, 176; min, max, 54, 431). Among the 30% most difficult-to-interpret cases, 39.2% were normal pCXR and 32.3% were cases of lobar pneumonia. Cases with a single trauma-related imaging finding, cardiac, hilar, and diaphragmatic pathologies were also among the most challenging. Discussion: At baseline, practicing physicians misdiagnosed about one-third of pCXR and there was up to an eight-fold difference between participants in number of cases completed to achieve the standardized performance benchmark. We also identified the diagnoses with the greatest potential for educational intervention.

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“…Several studies report the limitations of non-radiologists interpreting chest radiographs in children [ 20 ]. Given the worldwide shortage of radiologists, especially pediatric radiologists [ 6 ], this poses a problem particularly in countries with a high burden of TB.…”

Section: Interobserver Variability Of Chest Radiography Signsmentioning

confidence: 99%

Artificial Intelligence in Paediatric Tuberculosis

et al. 2023

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Tuberculosis (TB) continues to be a leading cause of death in children despite global efforts focused on early diagnosis and interventions to limit the spread of the disease. This challenge has been made more complex in the context of the coronavirus pandemic, which has disrupted the “End TB Strategy” and framework set out by the World Health Organization (WHO). Since the inception of artificial intelligence (AI) more than 60 years ago, the interest in AI has risen and more recently we have seen the emergence of multiple real-world applications, many of which relate to medical imaging. Nonetheless, real-world AI applications and clinical studies are limited in the niche area of paediatric imaging. This review article will focus on how AI, or more specifically deep learning, can be applied to TB diagnosis and management in children. We describe how deep learning can be utilised in chest imaging to provide computer-assisted diagnosis to augment workflow and screening efforts. We also review examples of recent AI applications for TB screening in resource constrained environments and we explore some of the challenges and the future directions of AI in paediatric TB.

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Interpretation of pediatric chest radiographs by non-radiologist clinicians in Botswana using World Health Organization criteria for endpoint pneumonia

Cited by 9 publications

References 26 publications

Viral and Bacterial Pneumonia Diagnosis via Deep Learning Techniques and Model Explainability

Viral and Bacterial Pneumonia Diagnosis via Deep Learning Techniques and Model Explainability

A Performance-Based Competency Assessment of Pediatric Chest Radiograph Interpretation Among Practicing Physicians

Artificial Intelligence in Paediatric Tuberculosis

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