We designed and developed an application (i-Interstitium) running on mobile devices to help radiologists with the diagnosis of diffuse lung diseases (DLDs). The application utilizes an algorithm that, based on the main radiological pattern, one or more radiological ancillary signs and optional clinical data, provides a list of possible diagnosis. The optional clinical data comprehends: patient¿s sex and age, smoke habit, signs and symptoms with their clinical onset and laboratory information.
The aim of the proposed research is to evaluate the role of i-Interstitium in the diagnosis of DLDs in a group of radiology residents and radiologists with low to moderate experience on high resolution computed tomography (HRCT) reporting.
Methods: twenty testers (10 radiologists and 10 radiology residents on the fourth and fifth year of residency) will be asked to evaluate 30 cases with known diagnosis of DLD. The cases will include HRCT images and relevant clinical information. Each tester should report the following data: main pattern and radiological ancillary signs on HRCT scans and a list of up to three possible differential diagnoses.
The main pattern and radiological ancillary signs reported by the tester and the clinical data will then be analyzed by the i-Interstitium¿s algorithm and the resulting diagnosis will be compared to the ones provided by the testers.
Data analysis: The data will be analyzed comparing the number of correct diagnosis obtained by tester with the number of correct diagnosis obtained with the help of i-Interstitium. Paired samples t-tests with a level of significance of
Design of the study: This will be a prospective study.
The rapid evolution of technology has allowed to have powerful portable devices continuously available. These devices are true pocket computers that can perform very complicated tasks in milliseconds and can handle a big amount of data, with simple and intuitive user interface. They can be very powerful tools in medical fields such as DLDs diagnosis, where a great number of clinical and radiological information must be matched. To our knowledge this is the first mobile application that can help the radiologist on diagnosis of DLDs, by combining several clinical and radiological information.