Modern radiology process a large amount of data compared to other health practices. X-rays, CT scans, MRI, Ultrasonography and other diagnostic images need to be stored and made available to all concerned healthcare professionals over the network. The traditional paper / film-based system cannot cope up with the current needs. It can take too long to locate the hard copies of the images and there is a possibility that images will be misfiled or outright lost. With the integration of smart software and healthcare IT smart tools are developed to stream line the workflow and make it hassle free.

Nowadays, radiology practices work with major software tools such as DICOM or PACS and RIS to streamline their effort and modernize their own operations. With PACS and RIS the images can be stored in the drive or cloud and can be sent to the doctor via the computer network, and allow the healthcare professionals with easier access to files in digital format.

PACS or Picture Archive and Communication System can store standard 2D images along with 3D images and is majorly used to store all diagnostic imaging files. This helps one to quickly research and search for information and retrieves the target image. This software can store CT scans, MRIs, and X-rays using local servers or a cloud computing platform and offers one with convenience and ease of expandability. This helps one to manage all the imagery that is need be to check the status of the patient that is going through treatment and recovery.

RIS or Radiology Information System is a specialized software solution that helps to easily combine reports of a patient with images that we take today along with the historical images, such as those taken to get a baseline. It helps automate data management and is specifically used in radiology departments. The software helps to optimize the imaging process by integrating various functions that are involved in patient management into one defined comprehensive system.
PACS and RIS are Now in Trend and More in Demand than Conventional Systems for Imaging.

Both RIS and PACS act as a complementary system and are integrated. RIS used in conjugation with PACS within radiology department/clinics, and the major work of the RIS is to improve the workflow and streamline the work process and PACS provide storage along with long term option for the management of the information of the patient and is integrated with features and tools for advanced manipulation of the images.

The government has also recognized the significance of health care IT and digitalization and is encouraging the usage of PACS and RIS, which allows in-depth and accurate analysis of the health of a patient. This not only helps in improved work ease, but offers management of a large number of patient records, and transparency in the medical insurance claim process.

PACS and RIS are proved to be the right choice for the future as they provide convenient access to economical storage of images from multiple modalities, and their application in several healthcare facilities like orthopedic, oncology, and cardiology is of high value.

PACS is been emerging as the leading segment both in terms of revenue and application with a huge demand and more spread and attraction in oncology, due to an increase in the incidences of cancer. Additionally, the rising demand for offering complete diagnoses is expected to fuel the growth of the segment.

According to Transparency Market Research’s latest report on the global PACS and RIS market for the historical period 2017–2018 and forecast period 2019–2027. The global PACS and RIS market is determined by increasing health concerns and growing demand for the digitization of products.

According to the latest information, the global PACS and RIS market was valued at US$ 2,633.4 million in 2018 and in terms of Volume 42,874 Units. The market value is expected to expand at a CAGR of 5.3% from 2019 to 2027.

Trivitron X-ray solutions with Integrated with Advanced Connectivity Software PACS & RIS

Trivitron Digital X-Ray products now features integrated PACS system with advanced AI Deep Learning features. Deep learning is based on a neural network structure that emulates to a great extent the working of the human brain. Neural network structures are designed to learn differential and delineating features from clinical data automatically, enabling them to approximate very complex nonlinear relationships. Once these neural network structures are trained with statistically significant samples of clinical images with expert radiologists specifying a structured approach towards identifying image patterns for a conclusive diagnosis, the software is able to emulate the human thinking process and predict diagnostic scores leaving the task of the confirmatory diagnosis to the expert eyes of the radiologist.