Artificial Intelligence Advances in the World of Cardiovascular Imaging

The tremendous advances in digital information and communication technology have entered everything from our daily lives to the most intricate aspects of medical and surgical care. These advances are seen in electronic and mobile health and allow many new applications to further improve and make the diagnoses of patient diseases and conditions more precise. In the area of digital radiology with respect to diagnostics, the use of advanced imaging tools and techniques is now at the center of evaluation and treatment. Digital acquisition and analysis are central to diagnostic capabilities, especially in the field of cardiovascular imaging. Furthermore, the introduction of artificial intelligence (AI) into the world of digital cardiovascular imaging greatly broadens the capabilities of the field both with respect to advancement as well as with respect to complete and accurate diagnosis of cardiovascular conditions. The application of AI in recognition, diagnostics, protocol automation, and quality control for the analysis of cardiovascular imaging modalities such as echocardiography, nuclear cardiac imaging, cardiovascular computed tomography, cardiovascular magnetic resonance imaging, and other imaging, is a major advance that is improving rapidly and continuously. We document the innovations in the field of cardiovascular imaging that have been brought about by the acceptance and implementation of AI in relation to healthcare professionals and patients in the cardiovascular field.

The Artificial Intelligence in Digital Radiology: Part 2: Towards an Investigation of acceptance and consensus on the Insiders

Background. The study deals with the introduction of the artificial intelligence in digital radiology. There is a growing interest in this area of scientific research in acceptance and consensus studies involving both insiders and the public, based on surveys focused mainly on single professionals. Purpose. The goal of the study is to perform a contemporary investigation on the acceptance and the consensus of the three key professional figures approaching in this field of application: (1) Medical specialists in image diagnostics: the medical specialists (MS)s; (2) experts in physical imaging processes: the medical physicists (MP)s; (3) AI designers: specialists of applied sciences (SAS)s. Methods. Participants (MSs = 92: 48 males/44 females, averaged age 37.9; MPs = 91: 43 males/48 females, averaged age 36.1; SAS = 90: 47 males/43 females, averaged age 37.3) were properly recruited based on specific training. An electronic survey was designed and submitted to the participants with a wide range questions starting from the training and background up to the different applications of the AI and the environment of application. Results. The results show that generally, the three professionals show (a) a high degree of encouraging agreement on the introduction of AI both in imaging and in non-imaging applications using both standalone applications and/or mHealth/eHealth, and (b) a different consent on AI use depending on the training background. Conclusions. The study highlights the usefulness of focusing on both the three key professionals and the usefulness of the investigation schemes facing a wide range of issues. The study also suggests the importance of different methods of administration to improve the adhesion and the need to continue these investigations both with federated and specific initiatives.

Iliopectineal line: A valuable radiological reference for spino-pelvic parameters

Objectives: Spinopelvic parameters are crucial to address sagittal spinal imbalance; such measurements require standardized lateral radiographs that include spine and hips, which are neither always available, nor readily feasible intra-operatively. The aim of this study was to describe pelvic radiological reference points that could provide reliable sagittal balance estimates from conventional lumbosacral lateral radiographs. Methods: A descriptive, cross-sectional, radiological-based study was conducted. Readings were taken from institute’s digital radiology library, blinded to personal and clinical data. The correlation was made to conventional pelvic incidence (CPI), conventional pelvic tilt (CPT), and sacral slope (SS), measured for the same patients, and from the same standardized standing radiographs that included femoral heads. Results: Radiological images for 140 adult subjects, with suspected or established spine problems were studied. The average lumbar lordosis (LL) of 3 readers was 47 ± 13 (13–81) with an interclass agreement of 0.9, SS was 41 ± 9 with an interclass agreement of 0.9, CPI was 53 ± 10 with an interclass agreement of 0.8, CPT was 14 ± 8 with an interclass agreement of 0.9, iliopectineal inclination (IPI) of 4 readers was 64 ± 8 with an interclass agreement of 0.7 and iliopectineal tilt (IPT) was 24 ± 8 with an interclass agreement of 0.8 LL was with 6° of CPI and 16° of IPI. The CPI was equal to (CPI = SS + [CPT + 1.2]) and (IPI = SS + [IPT + 0.6]). The IPI was negatively correlated with CPI –0.2 P = 0.006, and IPI was negatively correlated with CPT –0.333 P < 0.001. Conclusion: Iliopectineal line provides reproducible readings, closer values to LL, and addresses the center of mass displacement.

An Approach for Online Determining the Entrance Surface Air Kerma (ESAK) in Digital Radiology

An online method is proposed to determine the entrance surface air kerma (ESAK) in digital radiology from console-displayed kerma area product (PKA) data. ESAK values were calculated from X-ray tube outputs and patient exposure factors across five X-ray examinations. The corresponding PKAvalues were taken from the Digital Imaging and Communications in Medicine (DICOM) header. Using linear regression between ESAK and values, the slope and intercept coefficients for each type of X-ray equipment and procedure were determined. The coefficient to determine ESAK from ranged from 59% for a posteroanterior chest to 88% for anteroposterior lumbar spine view X-ray procedures. The results demonstrated the possibility of online estimates of ESAK from a console that displayed using readily available digital information in radiology. The results may have important implications in interventional radiology, where ESAK values are crucial for preventing skin injuries due to prolonged fluoroscopy times.

Study on canine hip dysplasia and its diagnosis by imaging and molecular genetics techniques

The present paper is a bibliographical research on canine hip dysplasia using imaging and molecular genetics techniques. Ever since the first description in 1935 made by Schnelle, canine hip dysplasia has remained one of the most diagnosed orthopedic conditions present in the dog breeds. The gold standard method of diagnosis of hip dysplasia is the radiological examination in hip-extended position. While the radiological examination focuses mainly on the individual for the diagnosis of this condition, methods of diagnosis of an entire population are sought, and these methods are represented by molecular genetics techniques. Naming the etiology of canine hip dysplasia and finding out the latest methods of genetically and radiologically diagnosis of canine hip dysplasia and the best alternatives of treatment for this disease. Canine hip dysplasia continues to be a major problem for owners, breeders and veterinarians. Currently, there are five standardized systems worldwide that deal with the grading of canine hip dysplasia. In addition to digital radiology, CT and ultrasonographic examination are feasible diagnosis methods. Even if the standard method of diagnosis remains the radiological examination in the present, the near future provides to be of the molecular genetic techniques.

Lessons from the COVID-19 Pandemic on the Use of Artificial Intelligence in Digital Radiology: The Submission of a Survey to Investigate the Opinion of Insiders

The development of artificial intelligence (AI) during the COVID-19 pandemic is there for all to see, and has undoubtedly mainly concerned the activities of digital radiology. Nevertheless, the strong perception in the research and clinical application environment is that AI in radiology is like a hammer in search of a nail. Notable developments and opportunities do not seem to be combined, now, in the time of the COVID-19 pandemic, with a stable, effective, and concrete use in clinical routine; the use of AI often seems limited to use in research applications. This study considers the future perceived integration of AI with digital radiology after the COVID-19 pandemic and proposes a methodology that, by means of a wide interaction of the involved actors, allows a positioning exercise for acceptance evaluation using a general purpose electronic survey. The methodology was tested on a first category of professionals, the medical radiology technicians (MRT), and allowed to (i) collect their impressions on the issue in a structured way, and (ii) collect their suggestions and their comments in order to create a specific tool for this professional figure to be used in scientific societies. This study is useful for the stakeholders in the field, and yielded several noteworthy observations, among them (iii) the perception of great development in thoracic radiography and CT, but a loss of opportunity in integration with non-radiological technologies; (iv) the belief that it is appropriate to invest in training and infrastructure dedicated to AI; and (v) the widespread idea that AI can become a strong complementary tool to human activity. From a general point of view, the study is a clear invitation to face the last yard of AI in digital radiology, a last yard that depends a lot on the opinion and the ability to accept these technologies by the operators of digital radiology.

The Artificial Intelligence in Digital Pathology and Digital Radiology: Where Are We?

Thanks to the incredible changes promoted by Information and Communication Technology (ICT) conveyed today by electronic-health (eHealth) and mobile-health (mHealth), many new applications of both organ and cellular diagnostics are now possible [...]

Evaluation of Exposure Index Values for Conventional Radiology Examinations: Retrospective Study in Governmental Hospitals at West Bank, Palestine

In the digital radiology system, radiologic technologists (RTs) can choose imaging parameters to include kVp and mAs. The RTs received feedback after acquisition of an image in the form of Exposure Index (EI). The aim of the current study was to check if the EI values are within the range values recommended by the manufacturer (MREI) for radiological examinations that include the chest, abdomen, pelvis, spine, and extremities. Data was collected from 3,000 adult X-ray examinations taken from several government hospitals in Palestine. The information included patient gender, kVp, mAs, EI values, and examination time. All examinations included in the study used the grid. While the study excluded all images that contained an implant or prosthesis. Descriptive statistical analysis was used to analyze the data, while the Mann–Whitney U test was used to detect statistically significant differences, P < 0.05. Some examinations showed the EI values outside the MREI ranges. The EIs in the chest AP examination were higher in the female group than males while other examinations have no difference between males and females. The EIs out of working hours were higher than in working hours, especially in the chest (P <0.0001), abdominal (P <0.0001), pelvic (P =0.02), and spine (P =0.0005) exams. In the summary, it has been proven that some of the examinations are outside the MREIs, with differences between the patient gender and the time of the examination. The retrospective study for the exposure index is very important in reducing the risk of radiation to patients.

Challenges for Transformative Innovation in Emerging Digital Health Organizations: advocating service design to address the multifaceted healthcare ecosystem

This article uses mini- case studies of three early stage organizations that pursued different pathways or models for bringing emerging, transformative digital technologies to the healthcare market. These organizations were each focused on different applications of digital health: Stentor was a venture capital backed, university spinoff focused in the field of digital radiology; Omnyx was formed as a joint venture (JV) by an academic medical center and industrial partner to transform the field of digital pathology; and, IBM Watson operating as an IBM unit, focused on the promise of artificial intelligence and machine learning for broad uses in cancer diagnosis and treatment. Each took a different organizational and business model path that resulted in mixed outcomes. While there are always many reasons for success or failure, we observe that these digital healthcare markets are more complex than typical consumer or technology markets. While any solution in healthcare demands patient centricity; healthcare markets additionally require a strong understanding and appreciation of the supporting ecosystem or network consisting of physicians and providers; and of constraints from payers and regulators. The value propositions of each member of the ecosystem must be understood and addressed. To meet this challenge, we advocate the formation of an integrated multidisciplinary commercialization team that addresses the multidimensional value proposition across the company life cycle. And importantly, that team should work collaboratively, and include service design as a key team member - along with the technology, business, marketing, reimbursement, and regulatory components.