The Use of Mobile Applications for the Diagnosis and Treatment of Tumors in Orthopaedic Oncology - a Systematic Review

Usefulness of Mobile Devices in Learning Process for Residents of Pediatric Surgical Specialties

INTRODUCTION

In the hospital setting, the use of mobile devices among surgical residents is increasing. To assess the usefulness of mobile devices for residents of pediatric surgical specialties.

MATERIALS AND METHODS

The study used a mixed-method design. First, a self-developed online questionnaire with 23 items was used to obtain quantitative data, which was analyzed via simple discriminant analysis. Qualitative data were obtained using the focus group technique with the subsequent triangulation of quantitative and qualitative data.

RESULTS

The residents used mobile devices for learning and communication. Using quantitative data, the study found that the major functions of mobile devices were communicating with other residents and taking clinical photos, and that for learning, were speaking with attendings, residents, collecting patient information, and searching for unfamiliar terms. Triangulation analysis confirmed that mobile devices aid in agile communication, the search for data on drugs or diseases, and consultation of medical applications. Qualitative data informed the limitations of devices and the inconsistencies between official regulations and their advantages in clinical practice.

CONCLUSIONS

We demonstrate the usefulness of mobile devices among surgical residents in clinical care and recommend that hospitals should regulate policies to maximize the use of mobile devices.

Apps in Clinical Practice: Usage Behaviour of Trauma Surgeons and Radiologists in Northern Germany

Introduction

Apps, in general, are an integral part of our daily lives. To investigate the current usage behaviour of trauma surgeons and radiologists regarding medical apps in clinical practice and to find out if and how the current range of medical apps can be improved, we surveyed trauma surgeons and radiologists in northern Germany. Material and Methods. An online questionnaire was sent to 100 trauma surgeons and 100 radiologists in northern Germany. Participants were asked about the frequency of their use of medical apps in clinical practice, which apps were used most often, how useful participants thought apps were, and in which area they would like to see improvements. The most frequently mentioned apps were finally analyzed.

Results

The survey study showed that 87.4% of the trauma surgeons and 67.4% of the radiologists use medical apps on a regular basis at work. It also revealed that trauma surgeons used medical apps much more often than radiologists and that young doctors were more likely to rely on medical apps than chief physicians. 80.0% of the participants would pay at least 5 euros for a medical app. Trauma surgeons see the greatest need for support in their daily work from medical apps in the area of treatment, while radiologists seek more support in the area of classification.

Conclusion

The study underscored the broad acceptance of medical apps in everyday clinical practice. As the physicians are willing to spend money and stated a general interest and need for further developments, there is high potential for the future. This trial is registered with DRKS00026766.

Human Monkeypox Classification from Skin Lesion Images with Deep Pre-trained Network using Mobile Application

Recently, human monkeypox outbreaks have been reported in many countries. According to the reports and studies, quick determination and isolation of infected people are essential to reduce the spread rate. This study presents an Android mobile application that uses deep learning to assist this situation. The application has been developed with Android Studio using Java programming language and Android SDK 12. Video images gathered through the mobile device’s camera are dispatched to a deep convolutional neural network that runs on the same device. Camera2 API of the Android platform has been used for camera access and operations. The network then classifies images as positive or negative for monkeypox detection. The network’s training has been carried out using skin lesion images of monkeypox-infected people and other skin lesion images. For this purpose, a publicly available dataset and a deep transfer learning approach have been used. All training and testing steps have been applied on Matlab using different pre-trained networks. Then, the network that has the best accuracy has been recreated and trained using TensorFlow. The TensorFlow model has been adapted to mobile devices by converting to the TensorFlow Lite model. The TensorFlow Lite model has been then embedded into the mobile application together with the TensorFlow Lite library for monkeypox detection. The application has been run on three devices successfully. During the run-time, the inference times have been gathered. 197 ms, 91 ms, and 138 ms average inference times have been observed. The presented system allows people with body lesions to quickly make a preliminary diagnosis. Thus, monkeypox-infected people can be encouraged to act rapidly to see an expert for a definitive diagnosis. According to the test results, the system can classify the images with 91.11% accuracy. In addition, the proposed mobile application can be trained for the preliminary diagnosis of other skin diseases.

Traumatology: Adoption of the Sm@rtEven Application for the Remote Evaluation of Patients and Possible Medico-Legal Implications

Telemedicine is the combination of technologies and activities that offer new remote ways of medical care. The Sm@rtEven application project is a remote assistance service that follows patients affected by lower limb fractures surgically treated at Galeazzi Orthopedic Institute (Milan, Italy). The Sm@rtEven application aims to evaluate the clinical conditions of patients treated for lower limb fracture after discharge from hospital using remote follow-up (FU). The project is not a substitute for traditional clinical consultations but an additional tool for a more complete and prolonged view over time. The Sm@rtEven application is installed on patients’ smartphones and is used daily to communicate with healthcare personnel. In the first protocol, patients had to complete different tasks for 30 days, such as monitoring the load progression on the affected limb, the number of steps during the day, and body temperature and completing a questionnaire. A simplified protocol was proposed due to the pandemic and logistical issues. The revised protocol enrolled patients after more than 30 days of their operation, prioritized the rehabilitation phase, and required patients to use the app for fewer days. After an initial phase of correct use, a reduction in patient compliance was gradually reported in the first protocol. However, patient compliance in the second protocol remained high (96.25%) in the recording of all the required parameters. The Sm@rtEven application has proven to be a valuable tool for following patients remotely, especially during the pandemic. Telemedicine has the same value as traditional clinical evaluations, and it enables patients to be followed over long distances and over time, minimizing any discomfort.