Anatomical Ignorance Resulting in Iatrogenic Causes of Human Morbidity

This article discusses how inadequate anatomy education contributes to iatrogenic causes of human morbidity and mortality. Through a review of the relevant literature, high-yield clinical cases were identified in which a lack of sufficient anatomical knowledge contributed to patient morbidity, such as abscess formation and neuropathy as a result of improper intramuscular injections, superior gluteal nerve injuries due to surgical procedures, and misdiagnoses due to physicians' inability to examine and correlate clinical and radiological findings. The importance of a multimodal learning approach in anatomy education for medical students, which includes the utilization of the cadaveric dissection approach to emphasize spatial understanding, is crucial for the development of competent physicians with a deep-rooted foundational knowledge of anatomy and related concepts, such as physiology, pathology, and radiology. It cannot be understated that anatomy education and a lack of knowledge of anatomy and related concepts may influence iatrogenic causes of human morbidity and mortality. Therefore, all efforts should be made to ensure that students develop a strong foundational anatomy knowledge during their preclinical years.

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.

Perceptual omission errors in positron emission tomography and computed tomography reporting

BACKGROUND

Omission errors in medical imaging can lead to missed diagnosis and harm to patients. The subject has been studied in conventional imaging, but no data is available for functional imaging in general and for PET/CT in particular. In this work, we evaluated the frequency and characteristics of perceptual omission errors in the PET component of oncologic PET/CT imaging, and we analyzed the hazardous scenarios prone to such modality-specific errors.

METHODS

Perceptual omission errors were collected in one tertiary center PET/CT clinic during routine PET/CT reporting over a 26-month period. The omissions were detected either in reporting follow-up PET/CT studies of the same patient or during multidisciplinary meetings.

RESULTS

Significant omission errors were found in 1.2% of the 2100 reports included in the study. The most common omissions were bone metastases and focal colon uptake. We identified six PET-specific causative factors contributing to the occurrence of omissions, and we propose solutions to minimize their influence.

CONCLUSIONS

The data presented here can help to promote the awareness of interpreting physicians to body areas that require higher attention and to implement reading strategies for improving the accuracy of PET/CT interpretation.

Reducing Errors Resulting From Commonly Missed Chest Radiography Findings

Chest radiography (CXR), the most frequently performed imaging examination, is vulnerable to interpretation errors resulting from commonly missed findings. Methods to reduce these errors are presented. A practical approach using a systematic and comprehensive visual search strategy is described. The use of a checklist for quality control in the interpretation of CXR images is proposed to avoid overlooking commonly missed findings of clinical importance. Artificial intelligence is among the emerging and promising methods to enhance detection of CXR abnormalities. Despite their potential adverse consequences, errors offer opportunities for continued education and quality improvements in patient care, if managed within a just, supportive culture.

Observer performance evaluation of the feasibility of a deep learning model to detect cardiomegaly on chest radiographs

Background

Cardiothoracic ratio (CTR) is the ratio of the diameter of the heart to the diameter of the thorax. An abnormal CTR (>0.55) is often an indicator of an underlying pathological condition. The accurate prediction of an abnormal CTR chest X-rays (CXRs) aids in the early diagnosis of clinical conditions.

Purpose

We propose a deep learning (DL)-based model for automatic CTR calculation to assist radiologists with rapid diagnosis of cardiomegaly and thus optimise the radiology flow.

Material and Methods

The study population included 1012 posteroanterior CXRs from a single institution. The Attention U-Net DL architecture was used for the automatic calculation of CTR. An observer performance test was conducted to assess the radiologist’s performance in diagnosing cardiomegaly with and without artificial intelligence assistance.

Results

U-Net model exhibited a sensitivity of 0.80 [95% CI: 0.75, 0.85], specificity >99%, precision of 0.99 [95% CI: 0.98, 1], and a F1 score of 0.88 [95% CI: 0.85, 0.91]. Furthermore, the sensitivity of the reviewing radiologist in identifying cardiomegaly increased from 40.50% to 88.4% when aided by the AI-generated CTR.

Conclusion

Our segmentation-based AI model demonstrated high specificity (>99%) and sensitivity (80%) for CTR calculation. The performance of the radiologist on the observer performance test improved significantly with provision of AI assistance. A DL-based segmentation model for rapid quantification of CTR can therefore have significant potential to be used in clinical workflows by reducing radiologists’ burden and alerting to an abnormal enlarged heart early on.

Weakly supervised pneumonia localization in chest X-rays using generative adversarial networks

PURPOSE

Automatic localization of pneumonia on chest X-rays (CXRs) is highly desirable both as an interpretive aid to the radiologist and for timely diagnosis of the disease. However, pneumonia's amorphous appearance on CXRs and complexity of normal anatomy in the chest present key challenges that hinder accurate localization. Existing studies in this area are either not optimized to preserve spatial information of abnormality or depend on expensive expert-annotated bounding boxes. We present a novel generative adversarial network (GAN)-based machine learning approach for this problem, which is weakly supervised (does not require any location annotations), was trained to retain spatial information, and can produce pixel-wise abnormality maps highlighting regions of abnormality (as opposed to bounding boxes around abnormality).

METHODS

Our method is based on the Wasserstein GAN framework and, to the best of our knowledge, the first application of GANs to this problem. Specifically, from an abnormal CXR as input, we generated the corresponding pseudo normal CXR image as output. The pseudo normal CXR is the "hypothetical" normal, if the same abnormal CXR were not to have any abnormalities. We surmise that the difference between the pseudo normal and the abnormal CXR highlights the pixels suspected to have pneumonia and hence is our output abnormality map. We trained our algorithm on an "unpaired" data set of abnormal and normal CXRs and did not require any location annotations such as bounding boxes/segmentations of abnormal regions. Furthermore, we incorporated additional prior knowledge/constraints into the model and showed that they help improve localization performance. We validated the model on a data set consisting of 14 184 CXRs from the Radiological Society of North America pneumonia detection challenge.

RESULTS

We evaluated our methods by comparing the generated abnormality maps with radiologist annotated bounding boxes using receiver operating characteristic (ROC) analysis, image similarity metrics such as normalized cross-correlation/mutual information, and abnormality detection rate.We also present visual examples of the abnormality maps, covering various scenarios of abnormality occurrence. Results demonstrate the ability to highlight regions of abnormality with the best method achieving an ROC area under the curve (AUC) of 0.77 and a detection rate of 85%.The GAN tended to perform better as prior knowledge/constraints were incorporated into the model.

CONCLUSIONS

We presented a novel GAN based approach for localizing pneumonia on CXRs that (1) does not require expensive hand annotated location ground truth; and (2) was trained to produce abnormality maps at the pixel level as opposed to bounding boxes. We demonstrated the efficacy of our methods via quantitative and qualitative results.

Computational modeling of human reasoning processes for interpretable visual knowledge: a case study with radiographers

Visual reasoning is critical in many complex visual tasks in medicine such as radiology or pathology. It is challenging to explicitly explain reasoning processes due to the dynamic nature of real-time human cognition. A deeper understanding of such reasoning processes is necessary for improving diagnostic accuracy and computational tools. Most computational analysis methods for visual attention utilize black-box algorithms which lack explainability and are therefore limited in understanding the visual reasoning processes. In this paper, we propose a computational method to quantify and dissect visual reasoning. The method characterizes spatial and temporal features and identifies common and contrast visual reasoning patterns to extract significant gaze activities. The visual reasoning patterns are explainable and can be compared among different groups to discover strategy differences. Experiments with radiographers of varied levels of expertise on 10 levels of visual tasks were conducted. Our empirical observations show that the method can capture the temporal and spatial features of human visual attention and distinguish expertise level. The extracted patterns are further examined and interpreted to showcase key differences between expertise levels in the visual reasoning processes. By revealing task-related reasoning processes, this method demonstrates potential for explaining human visual understanding.

The role of clinical history in the interpretation of chest radiographs

OBJECTIVE

This review will appraise the literature pertaining to the influences that clinical history has on the action of assessing the chest radiograph.

KEY FINDINGS

There remains conflicting evidence on the impact of clinical history on chest radiography. Some research suggests that clinical history has the potential to influence the reporter in a negative way by limiting their search strategy to a more focussed search. Image interpretation is more accurate when reporters are allowed to conduct a free search of the chest image, untainted by preconceived concepts.

CONCLUSION

Clinical history needs to be accessed appropriately to aid and not stifle accurate image interpretation. Reporters need to be aware of the potential bias clinical history can introduce to their reporting and develop strategies to alleviate this as much as possible.

IMPLICATIONS FOR PRACTICE

A greater understanding of the potential bias of clinical history on the process of image interpretation is required by all reporters. Reporters need to develop an approach and strategy when accessing clinical history. Novice reporters need to be educated regarding the impact of clinical history on their reporting.

High prevalence of abnormalities on chest radiography in rheumatoid arthritis

INTRODUCTION

Chest radiography (CXR) is commonly performed in rheumatoid arthritis (RA), particularly for the diagnosis of pulmonary disease. However, other structures are visible on CXR, abnormalities of which may contribute to morbidity and early mortality. This study was undertaken to evaluate the extent of CXR abnormalities in RA patients.

METHODS

Consecutive out-patients meeting the 2010 ACR/EULAR classification criteria for RA were included. The most recent CXR was assessed by two independent reviewers. Abnormalities identified were recorded and compared to the formal CXR report. Predictors of abnormalities on CXR were assessed using chi-squared tests. SPSS 18.0 was used for statistical analysis.

RESULTS

One hundred and ninety-eight patients were included. Mean age was 62 years (range 18-90). One hundred and nine (55.1%) were current or ex-smokers. One hundred and fifty-six (79%) patients were seropositive and 123 (62.1%) had joint erosions. A recent CXR was available in 163 (82%) cases. Abnormalities were identified in 129 (79.1%). Ninety-seven (60%) had bony abnormalities. Seventy-one (43.6%) had pulmonary abnormalities; old tuberculosis in 34 (20.9%), hyperinflation in 24 (14.7%), interstitial changes in 20 (13.3%), nodules in 4 (2.4%), consolidation in 2 (1.2%), and pneumothorax in 1 (0.6%). Cardiomegaly was identified in 37 (22.7%) and aortic calcification in 24 (14.7%). Age (p = 0.001), male gender (p = 0.01), and seropositivity (p = 0.04) were significantly associated with lung abnormalities. Cardiomegaly was associated with hypertension (p = 0.012) and ischaemic heart disease (p = 0.018).

CONCLUSION

Abnormalities were identified in 79% of chest radiographs in RA patients. Sixty-six percent of these were not reported. Clinicians need to be aware of the need to check for abnormalities.Key Points• RA patients have a high prevalence of CXR abnormalities.• Many of these are of clinical significance.• Age, being male, and seropositivity were associated with lung abnormalities.