Media messaging in diagnosis of acute CXR pathology: an interobserver study among residents

Mobile applications in radiology: own study based on polish data

As the number of smartphones increases, so does the number of medical apps. Medical mobile applications are widely used in many medical fields by both patients and doctors. However, there are still few approved mobile applications that can be used in the diagnostic-therapeutic process and radiological apps are affected as well. We conducted our research by classifying radiological applications from the Google Play® store into appropriate categories, according to our own qualification system developed by researchers for the purposes of this study. In addition, we also evaluated apps from the App Store®. The radiology application rating system we created has not been previously used in other articles. Out of 228 applications from the Google Play store, only 6 of them were classified as "A" category with the highest standard. Apps from the App Store (157) were not categorized due to the lack of download counts, which was necessary in our app-rating system. The vast majority of applications are for educational purposes and are not used in clinical practice. This is due to the need of obtaining special permits and certificates from relevant institutions in order to use them in medical practice. We recommend applications from the Google Play store that have been classified in the "A" category, evaluating them as the most valuable. App Store apps data is described and presented in the form of diagrams and tables.

Using smartphone to evaluate Cranial Computed Tomography videos: an inter-observer study

Many clinicians receive Cranial Computed Tomography (CCT) images or videos by their smartphone. The aim of this study was to evaluate the reliability of the CCT videos that are shared through smartphone in the diagnosis. The CCT videos that were sent via WhatsApp were examined in 9 sections: soft tissue, bone structure, parenchyma, ventricle, vascular structures, middle ear, orbits, sinuses and the extra axial space. The CCT videos were analyzed in 9 sections; there was a perfect agreement among specialists in one of these sections, good agreement in 6 and poor agreement in 2. When compared with the gold standard, it was shown that 5 out of 9 sections could be an alternative to the gold standard. It may be thought that evaluation of the CCT videos can be obtained with messenger applications such as WhatsApp, which is a cheap, fast and common application. But this study shows that diagnostic images and videos shared through the smartphone by a messenger application can not be an alternative to standard evaluations.

Your mileage may vary: impact of data input method for a deep learning bone age app's predictions

OBJECTIVE

The purpose of this study was to evaluate agreement in predictions made by a bone age prediction application ("app") among three data input methods.

METHODS

The 16Bit Bone Age app is a browser-based deep learning application for predicting bone age on pediatric hand radiographs; recommended data input methods are direct image file upload or smartphone-capture of image. We collected 50 hand radiographs, split equally among 5 bone age groups. Three observers used the 16Bit Bone Age app to assess these images using 3 different data input methods: (1) direct image upload, (2) smartphone photo of image in radiology reading room, and (3) smartphone photo of image in a clinic.

RESULTS

Interobserver agreement was excellent for direct upload (ICC = 1.00) and for photos in reading room (ICC = 0.96) and good for photos in clinic (ICC = 0.82), respectively. Intraobserver agreement for the entire test set across the 3 data input methods was variable with ICCs of 0.95, 0.96, and 0.57 for the 3 observers, respectively.

DISCUSSION

Our findings indicate that different data input methods can result in discordant bone age predictions from the 16Bit Bone Age app. Further study is needed to determine the impact of data input methods, such as smartphone image capture, on deep learning app performance and accuracy.

Influence of the file format and transmission app on the radiographic diagnosis of caries lesions

OBJECTIVES

This in vitro study aimed to evaluate the influence of the radiographic image file format and the transmission application (app) on the diagnosis of proximal caries lesions.

STUDY DESIGN

Twenty bitewing radiographs of 40 posterior human teeth placed in phantoms were acquired using the Digora Toto digital sensor. All images were exported as TIFF (Tagged Image File Format), BMP (Windows Bitmap), PNG (Portable Network Graphics), and JPEG (Joint Photographic Experts Group) and transmitted online via WhatsApp and Messenger. Five examiners evaluated the radiographs with no online transmission and as transmitted through the 2 apps for the presence of proximal caries lesions using a 5-point scale. The reference standard for caries lesions was established using micro-computed tomography. Two-way analysis of variance compared values of sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (α = .05). The kappa test was used to assess intra- and interexaminer agreement.

RESULTS

Sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve values showed no significant differences in the diagnosis of proximal caries lesions between the different image file formats (P ≥ .773) and transmission apps (P ≥ .608). Intraexaminer agreement was substantial (κ = 0.742) and interexaminer agreement was moderate (κ = 0.475).

CONCLUSION

The digital file format and transmission app did not influence the radiographic diagnosis of proximal caries lesions.

Smartphone evaluation of postero-anterior chest x-rays: An inter-observer study

BACKGROUND

Posterior-anterior chest x-ray (PA-CXR) is among the most commonly used imaging methods in the diagnosis both in the emergency departments (ED) and the other clinics. The aim of the present study was to evaluate the diagnostic reliability of PA-CXRs sent via a smartphone.

METHODS

This study was conducted as an inter-observer study. PA-CXRs were photographed with a smartphone and they were sent to two separate participants (emergency medicine specialists one with 4 years experience and another with 3) via the WhatsApp application. And the participants evaluated to these images on their mobile phone.

RESULTS

A poor concordance was determined in a ratio of 3/8 and good concordance was detected in a ratio of 3/8 between the two participants (p < 0.05). It was observed that only the mediastinum assessments could be an alternative to the gold standard (p < 0.01).

CONCLUSION

We may conclude that the assessments done via a smartphone (photographing and sharing) may not be reliable.