The common bottlenecks which might slow down your business growth
Reports are increasingly vulnerable to tampering and mishaps due to the abundance of outsourcing.
The number of professionals going into the field of pathology has been on the decline.
The requirement of expertise and testing protocols create various complications in the lab testing procedures.
The lack of involvement of pathologists in clinical processes and patient examination, causing an interference with holistic diagnosis.
Lab testing costs and operational expenses is increasingly hampering the quality of pathology.
Conventional procedures are preferred and a resistance to evolving technologies, causing low quality diagnosis.
Holistic diagnosis through AI pathology-related technology, featuring intelligent machines for digital imaging
Pathologists have long been using traditional diagnosis methods that are time consuming and prone to errors. AI pathology-based diagnosis uses automated diagnostic devices to offer a shorter turnaround time then traditional microscopic testing. Additionally, the possibility of errors is reduced through quality control of digital pathology software, along with a more holistic collaborative approach.
AI for pathology causes an instant transformation, wherein pathologists in the digital realm are no longer dealing with glass, but with pixels that can be sent to any part of the world for specialized examination. Through image analysis algorithms, AI in medical testing allows for automated analysis that is generated through the basis of research and recorded data in the software.
AI for pathology leverages the predictive analytics technology to assist clinicians to identify disease vulnerability prior to its onset. This creates a culture of preventive care as opposed to remedial care. Through intelligent machine learning, patients who are prone to a certain illness are flagged and therefore, capture the attention of the pathologist who can then advice long term care.
An AI-based pathology solution that uses neural elements to make the system identify objects that have been rarely or never seen. Through advanced algorithms, these objects are then analyzed and highlighted to the pathologist, along with a detailed account. The process is further enhanced through a suggested translation of the implication of the identified object.
An image segmentation technique is a distinguished feature of AI-based pathology technology, wherein mathematical morphology is applied to segregate images through the watershed transform and the homotopic modification. Intelligent and automated features of segmentation allow the pathologist to operate with higher efficacy.
This AI pathology solution offers holistic diagnosis by expanding its reach to capture the full-spectrum of the sample/ specimen on the slide, which is in direct contrast to the restricted view of microscopic study. The resultant data and report generation that is obtained from WSI technology is much more comprehensive than traditional methods and can also be used for historic recording purposes.
Digital ‘Whole Slide Imaging’ is made possible with AI in pathology. AI technology to gather digital pathology slides, analyze them and apply that knowledge to correctly identify tumor samples and estimate tumor percentages. Rare object identification can also be achieved with AI in Pathology.
Tissue phenomics is a way to structure images meaningfully and extract statistical data from relevant objects, regions, and textures in tissue. AI in pathology can help in tissue phenomics by quantifying sample tissue data to identify biomarkers and substances indicative of cancer. It also helps in integrating multi-omics data with advanced clinical outcomes with identifying patterns.
Diabetic retinopathy is a complication of diabetes that may lead to blindness. AI in Pathology can enhance the diagnostic systems that can identify the risk of developing diabetic retinopathy. Advanced deep learning technology can search for the lesions to the complication by processing each labeled image through a series of related filters.
AI in pathology has the potential to determine the hidden but informative patterns too subtle or complex for pathologists to diagnose. The breast cancer image retrieval of ‘malignant regions can be identified with AI methods with a sensitivity above 92%. This can help in reducing the workload of pathologists and assist with case triage.
Our AI pathology software solution offers image analysis based diagnosis that is radically superior in accuracy through standardization and automation features that are enabled into our computational pathology technology. The distinct advantage is the diminished susceptibility of human error that is so commonly notice in traditional pathology methods. Routine pathology tasks are infused with a higher level of efficacy and tedious procedures are promptly simplified. The automated function of computer-assisted diagnosis is poised to cause a disruptive transformation in the world of pathology.
Our technology solution uses AI-based algorithms for intelligent analysis that offer quantitative results of cell-by-cell tissue analysis with an unmatched accuracy. The cloud-based software offers multiple historically relevant data for comparison and identification. The generated high resolution data is classified cell-by-cell across an entire spectrum of the slide image, with multiple references to parameters. Our top-of-the-line image augmentation features are targeted toward providing a holistic picture to the pathologist.
Through the successful application of deep learning AI algorithms to the diagnosis of tumors, our technology rapidly detects cancer tumors. A dataset of digitized pathology slides are used as reference points to suggest the possibility of a benign or cancerous tumor. Our algorithms are routinely trained in increasing sensitivity of identifying tumors with a much higher accuracy rate than trained pathologists. Scanning machinery is deployed and customization features are embedded into the software.
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