Exploring AI-healthcare innovation: natural language processing-based patents analysis for technology-driven roadmapping

PurposeThe purposes of this paper are (1) to explore the overall development of AI technologies and applications that have been demonstrated to be fundamentally important in the healthcare industry, and their related commercialized products and (2) to identify technologies with promise as the basis of useful applications and profitable products in the AI-healthcare domain.Design/methodology/approachThis study adopts a technology-driven technology roadmap approach, combined with natural language processing (NLP)-based patents analysis, to identify promising and potentially profitable existing AI technologies and products in the domain of AI healthcare.FindingsRobotics technology exhibits huge potential in surgical and diagnostics applications. Intuitive Surgical Inc., manufacturer of the Da Vinci robotic system and Ion robotic lung-biopsy system, dominates the robotics-assisted surgical and diagnostic fields. Diagnostics and medical imaging are particularly active fields for the application of AI, not only for analysis of CT and MRI scans, but also for image archiving and communications.Originality/valueThis study is a pioneering attempt to clarify the interrelationships of particular promising technologies for application and related products in the AI-healthcare domain. Its findings provide critical information about the patent activities of key incumbent actors, and thus offer important insights into recent and current technological and product developments in the emergent AI-healthcare sector.

Digital Pathology: Advantages, Limitations and Emerging Perspectives

Digital pathology is on the verge of becoming a mainstream option for routine diagnostics. Faster whole slide image scanning has paved the way for this development, but implementation on a large scale is challenging on technical, logistical, and financial levels. Comparative studies have published reassuring data on safety and feasibility, but implementation experiences highlight the need for training and the knowledge of pitfalls. Up to half of the pathologists are reluctant to sign out reports on only digital slides and are concerned about reporting without the tool that has represented their profession since its beginning. Guidelines by international pathology organizations aim to safeguard histology in the digital realm, from image acquisition over the setup of work-stations to long-term image archiving, but must be considered a starting point only. Cost-efficiency analyses and occupational health issues need to be addressed comprehensively. Image analysis is blended into the traditional work-flow, and the approval of artificial intelligence for routine diagnostics starts to challenge human evaluation as the gold standard. Here we discuss experiences from past digital pathology implementations, future possibilities through the addition of artificial intelligence, technical and occupational health challenges, and possible changes to the pathologist’s profession.

P002: Minimum archiving requirements for emergency medicine point-of-care ultrasound: a modified Delphi-derived national consensus

Introduction: Point-of-care ultrasound (POCUS) has become standard practice in emergency departments ranging from remote rural hospitals to well-resourced academic centres. To facilitate quality assurance, the Canadian Association of Emergency Physicians (CAEP) recommends image archiving. Due in part to poor infrastructure and lack of a national standard, however, archiving remains uncommon. Our objective was to establish a minimum standard archiving protocol for the core emergency department POCUS indications. Methods: Itemization of potential archiving standards was created through an extensive literature review. An online, three-round, modified Delphi survey was conducted with the thirteen POCUS experts on the national CAEP Emergency Ultrasound Committee tasked with representing diverse practice locations and experiences. Participants were surveyed to determine the images or clips, measurements, mode, and number of views that should comprise the minimum standard for archiving. Consensus was pre-defined as 80%. Results: All thirteen experts participated fully in the three rounds. In establishing minimum image archiving standards for emergency department POCUS, complete consensus was achieved for first trimester pregnancy, hydronephrosis, cardiac activity versus standstill, lower extremity deep venous thrombosis, and ultrasound-guided central line placement. Consensus was achieved for the majority of statements regarding abdominal aortic aneurysm, extended focused assessment with sonography in trauma, pericardial effusion, left and right ventricular function, thoracic B-line assessment, cholelithiasis and cholecystitis scans. In total, consensus was reached for 58 of 69 statements (84.1%). This included agreement on 41 of 43 statements (95.3%) describing mandatory images for archiving in the above indications. Conclusion: Our modified Delphi-derived consensus represents the first national standard archiving requirements for emergency department POCUS. Depending on the clinical context, additional images may be required beyond this minimum standard to support a diagnosis.

CACTUS: cancer image annotating, calibrating, testing, understanding and sharing in breast cancer histopathology

Objective Develop CACTUS (cancer image annotating, calibrating, testing, understanding and sharing) as a novel web application for image archiving, annotation, grading, distribution, networking and evaluation. This helps pathologists to avoid unintended mistakes leading to quality assurance, teaching and evaluation in anatomical pathology. Effectiveness of the tool has been demonstrated by assessing pathologists performance in the grading of breast carcinoma and by comparing inter/intra-observer assessment of grading criteria amongst pathologists reviewing digital breast cancer images. Reproducibility has been assessed by inter-observer (kappa statistics) and intra-observer (intraclass correlation coefficient) concordance rates. Results CACTUS has been evaluated using a surgical pathology application—the assessment of breast cancer grade. We used CACTUS to present standardized images to four pathologists of differing experience. They were asked to evaluate all images to determine their assessment of Nottingham grade of a series of breast carcinoma cases. For each image, they were asked for their overall grade impression. CACTUS helps and guides pathologists to improve disease diagnosis with higher confidence and thereby reduces their workload and bias. CACTUS can be useful for both disseminating anatomical pathology images for teaching, as well as for evaluating agreement amongst pathologists or against a gold standard for evaluation or quality assurance.

Development of Algorithm for Preservation of Heritage Murals and Painting Using Image Processing

<span>Image archiving and preservation finds extensive application in culture heritage murals. The study of cultural heritage is of the extreme importance at national and international levels. Not only global organizations like UNESCO but also museums, libraries, culture, temples and private initiatives are working in these directions. During the last three decades, researchers in the field of imaging discipline have started to contribute an increasing set of algorithms for cultural heritage; in that way providing indispensable support to these efforts. A better comparison of the different compression methods presented in this proposed work for culture Heritage mural images. Compression methods usually applied some method to reduce the number of components within each spectrum. The effectiveness of mural image archiving and preservation is analyzed based on 2-D wavelets filtering. The optimum algorithm is also found based on the results.</span>

An Efficient Middle Layer Platform for Medical Imaging Archives

Digital medical image usage is common in health services and clinics. These data have a vital importance for diagnosis and treatment; therefore, preservation, protection, and archiving of these data are a challenge. Rapidly growing file sizes differentiated data formats and increasing number of files constitute big data, which traditional systems do not have the capability to process and store these data. This study investigates an efficient middle layer platform based on Hadoop and MongoDB architecture using the state-of-the-art technologies in the literature. We have developed this system to improve the medical image compression method that we have developed before to create a middle layer platform that performs data compression and archiving operations. With this study, a platform using MapReduce programming model on Hadoop has been developed that can be scalable. MongoDB, a NoSQL database, has been used to satisfy performance requirements of the platform. A four-node Hadoop cluster has been built to evaluate the developed platform and execute distributed MapReduce algorithms. The actual patient medical images have been used to validate the performance of the platform. The processing of test images takes 15,599 seconds on a single node, but on the developed platform, this takes 8,153 seconds. Moreover, due to the medical imaging processing package used in the proposed method, the compression ratio values produced for the non-ROI image are between 92.12% and 97.84%. In conclusion, the proposed platform provides a cloud-based integrated solution to the medical image archiving problem.