The effect of clinical history on chest radiograph interpretations in a PACS environment

Association between clinical history in the radiographic request and diagnostic accuracy of thorax radiographs in dogs: A retrospective case-control study

BACKGROUND

The effect of clinical history on the interpretation of radiographs has been widely researched in human medicine. There is, however, no data on this topic in veterinary medicine.

HYPOTHESIS/OBJECTIVES

Diagnostic accuracy would improve when history was supplied.

ANIMALS

Thirty client-owned dogs with abnormal findings on thoracic radiographs and confirmation of the disease, and 30 healthy client-owned controls were drawn retrospectively.

METHODS

Retrospective case-control study. Sixty radiographic studies of the thorax were randomized and interpreted by 6 radiologists; first, with no access to the clinical information; and a second time with access to all pertinent clinical information and signalment.

RESULTS

A significant increase in diagnostic accuracy was noted when clinical information was provided (64.4% without and 75.2% with clinical information; P = .002). There was no significant difference in agreement between radiologists when comparing no clinical information and with clinical information (Kappa 0.313 and 0.300, respectively).

CONCLUSIONS AND CLINICAL IMPORTANCE

The addition of pertinent clinical information to the radiographic request significantly improves the diagnostic accuracy of thorax radiographs of dogs and is recommended as standard practice.

Classification of Musculoskeletal Radiograph Requisition Appropriateness Using Machine Learning

Objective: Poor quality imaging requisitions lower report quality and impede good patient care. Manual control of such requisitions is time consuming and can be a source of friction with referring physicians. The purpose of this study was to determine if poor quality requisitions could be identified automatically using machine learning and natural language processing techniques in order to allow for more efficient workflow. Methods: Exam indications from 50 000 musculoskeletal radiograph requisitions were manually classified, reviewed and deemed 'appropriate' or 'inappropriate' by two staff radiologists based on ACR appropriateness criteria. The requisitions were divided into training and test groups (80/20 split). The training set was pre-processed, converted to a bag-of-words model and used to train a Multinomial Naïve Bayes classifier which was then applied to the test set. Results: Out of 50 000 requisitions, 12 253 (24.5%) were deemed to contain an inappropriate indication. A Naive Bayes model correctly classified requisitions with an accuracy of 98%. In the test set, 107 of 7561 (1.4%) appropriate requisitions were incorrectly flagged and 92 of 2439 (3.8%) inappropriate requisitions were not flagged. Conclusions: Accurate automated identification of inappropriate indications on musculoskeletal requisitions is feasible using machine learning and natural language processing.

Impact of clinical history on chest radiograph interpretation

BACKGROUND

The inclusion of clinical information may have unrecognized influence in the interpretation of diagnostic testing.

OBJECTIVE

The objective of the study was to determine the impact of clinical history on chest radiograph interpretation in the diagnosis of pneumonia.

DESIGN

Prospective case-based study.

METHODS

Radiologists interpreted 110 radiographs of children evaluated for suspicion of pneumonia. Clinical information was withheld during the first interpretation. After 6 months the radiographs were reviewed with clinical information. Radiologists reported on pneumonia indicators described by the World Health Organization (ie, any infiltrate, alveolar infiltrate, interstitial infiltrate, air bronchograms, hilar adenopathy, pleural effusion).

SETTING

Children's Hospital of Philadelphia and Boston Children's Hospital.

PARTICIPANTS

Six board-certified radiologists.

OUTCOME MEASURES

Inter- and inter-rater reliability were assessed using the kappa statistic.

RESULTS

The addition of clinical history did not have a substantial impact on the inter-rater reliability in the identification of any infiltrate, alveolar infiltrate, interstitial infiltrate, pleural effusion, or hilar adenopathy. Inter-rater reliability in the identification of air bronchograms improved from fair (k = 0.32) to moderate (k = 0.53). Intra-rater reliability for the identification of alveolar infiltrate remained substantial to almost perfect for all 6 raters with and without clinical information. One rater had a decrease in inter-rater reliability from almost perfect (k = 1.0) to fair (k = 0.21) in the identification of interstitial infiltrate with the addition of clinical history.

CONCLUSIONS

Alveolar infiltrate and pleural effusion are findings with high intra- and inter-rater reliability in the diagnosis of bacterial pneumonia. The addition of clinical information did not have a substantial impact on the reliability of these findings.

Inference and uncertainty in radiology

This paper seeks to enhance understanding of the philosophical underpinnings of our discipline and the resulting practical implications. Radiology reports exist in order to convey new knowledge about a patient's condition based on empiric observations from anatomic or functional images of the body. The route to explanation and prediction from empiric evidence is mostly through inference based on inductive (and sometimes abductive) arguments. The conclusions of inductive arguments are, by definition, contingent and provisional. Therefore, it is necessary to deal in some way with the uncertainty of inferential conclusions (i.e. interpretations) made in radiology reports. Two paradigms for managing uncertainty in natural sciences exist in dialectic tension with each other. These are the frequentist and Bayesian theories of probability. Tension between them is mirrored during routine interactions among radiologists and clinicians. I will describe these core issues and argue that they are quite relevant to routine image interpretation and reporting.