Medical 3D visualization Statistics, Trends And Facts 2023

Medical 3D visualization is a powerful tool that allows doctors and medical professionals to see inside the human body in a way that was never before possible.

This blog post will explore medical 3D visualization statistics that provide insight into the current trends, adoption, market analysis, demographics, and more of this cutting-edge technology.

Medical 3D Visualization Trends

1. The use of medical 3D visualization technology is increasing, with a projected 20% annual growth rate.
2. Medical 3D visualization is being used in a wide range of medical specialties including surgery, radiology, and pathology.
3. Medical 3D visualization is also being used in the pharmaceutical and biotechnology industries for drug development and testing.
4. Medical 3D visualization is being used to improve patient outcomes by allowing doctors to plan and perform surgeries with greater precision.

Medical 3D Visualization Adoption

5. 80% of medical institutions now use medical 3D visualization technology.
6. 95% of all medical imaging studies are now interpreted using 3D visualization techniques.
7. The adoption of medical 3D visualization technology is not limited to hospitals, with clinics, private practices, and research institutions also adopting the technology.
8. The use of medical 3D visualization in surgical planning and training is expected to increase by 30% in the next 5 years.

Medical 3D Visualization Market Analysis

9. The global medical 3D visualization market is expected to reach $4 billion by 2025, with a CAGR of 13%.
10. The market for medical 3D visualization software is projected to grow at a CAGR of over 15% during the forecast period.
11. The market is driven by the increasing adoption of medical 3D visualization in surgical planning and training, as well as the growing use of medical 3D visualization in the pharmaceutical and biotechnology industries.

Medical 3D Visualization Demographics

12. Medical 3D visualization technology is being used by doctors and medical professionals of all ages and genders.
13. The use of medical 3D visualization technology is not limited to any particular geographic region, with the technology being used globally.
14. Medical 3D visualization technology is being used to treat patients of all ages, from infants to the elderly.

Medical 3D Visualization Advancement

15. The technology behind medical 3D visualization has advanced significantly in recent years, resulting in more accurate and detailed images.
16. The use of machine learning and AI in medical 3D visualization is increasing, resulting in even more accurate and efficient image analysis.
17. Medical 3D visualization is now being used in combination with other technologies, such as virtual reality and augmented reality, to create even more immersive and interactive medical experiences.
18. The use of medical 3D visualization for remote consultations is also growing, allowing doctors to remotely view and analyze medical images and provide remote diagnoses and treatment plans.

Medical 3D Visualization Education and Training

19. Medical 3D visualization technology is being used in medical education and training, allowing students to see and interact with 3D models of the human body.
20. The use of medical 3D visualization technology in education and training is resulting in more effective and efficient learning for students.
21. Medical 3D visualization technology is also being used in continuing medical education, allowing doctors to stay current with the latest medical developments and treatments.

Medical 3D Visualization Research and Development

22. Medical 3D visualization technology is being used in research and development to improve the understanding of human anatomy and disease.
23. Medical 3D visualization technology is being used in the development of new drugs and treatments.
24. Medical 3D visualization technology is being used in the study of rare and complex medical conditions, allowing for more in-depth analysis and understanding.

Medical 3D Visualization Cost-Effectiveness

25. Medical 3D visualization technology has been shown to be cost-effective, with studies indicating a reduction in medical costs for patients who have undergone procedures using 3D visualization.
26. The use of medical 3D visualization technology has also been shown to reduce the need for multiple surgeries and repeat procedures, further reducing costs for patients and the healthcare system.
27. The use of medical 3D visualization technology in surgical planning and training can also reduce the need for expensive and time-consuming cadaveric training.
28. The use of medical 3D visualization technology in remote consultations can also reduce the need for travel and in-person visits, resulting in cost savings for patients and healthcare providers.

Medical 3D Visualization Limitations

29. Although medical 3D visualization technology has many benefits, there are also some limitations to consider.
30. The cost of the technology and equipment can be a barrier for some healthcare providers and institutions.
31. There is a need for skilled personnel to operate and interpret the images generated by medical 3D visualization technology.
32. There is also a need for ongoing maintenance and upgrades to ensure the technology remains accurate and up-to-date.

Medical 3D Visualization Market Statistics

33. The global 3D medical imaging market is predicted to reach $15. 
34. Artificial Intelligence’s application in the medical imaging market is predicted to grow up to $264.85 billion by 2026. 
35. The global 3D medical imaging services market was valued at $207,134.9 million in 2020, and is projected to reach $377,062.6 million by 2030, registering a CAGR of 6.6% from 2021 to 2030. 
36. According to technique, the MRI segment dominated the market in 2020, and this trend is expected to continue during the forecast period, owing to advancements in MRI technology. 
37. The global market for manufactured devices was estimated at $5 billion in 2018. 
38. In the United States, as estimate as of 2015 places the US market for imaging scans at about $100b, with 60% occurring in hospitals and 40% occurring in freestanding clinics, such as the RadNet. 

Medical 3D Visualization Latest Statistics

39. Geometric prmeters such s dimeters D nd ortic rch height A nd width T were mesured mnully on 2D CMR imge slices ccording to [17] nd [24]. 
40. Results showed that template calculation time can be reduced by up to 85 % if an appropriately low mesh resolution is chosen without substantially affecting the final template shape. 
41. A cut off value for tolerable surface errors was chosen to be 0.5 % compared to the original subject mesh, which was reached for a surface mesh resolution of 0.75 cells/mm2. 
42. in the order of magnitude of the shape features to be captured [12]; however, clear indication for parameter setting is missing, in particular for the stiffness λV, which cannot be intuitively estimated. 
43. and λV can be initialised using for a given percentage pW or pV, respectively. 
44. Here, we set pW to 2.5 % and pV to 25 %, which yielded an initial λW of 15 mm and a λV of 47 mm, with the minimal surface area present in the set of shapes being Asurf,min = 8825 mm2. 
45. The was then transformed towards the smallest subject while incrementally decreasing λW and λV in 1 mm steps until the matching error between source and target was reduced by ≥80 %. 
46. A perfect (100 % error reduction). 
47. A template shape yielding a low overall deviation ∆Devtotal from population mean values of below 5 % was considered to represent a good approximation of the mean shape. 
48. Overall average deviation from those mean geometric population values was 3.1 %. 
49. Using gross geometric parameters , cross validation templates showed average total deviations from the original template ranging from 2.8 to 6.6 %. 
50. Thus, CoA20 is likely to skew the subsequent shape feature extraction and was therefore removed from the following analyses. 
51. Subsequent PLS regression with BSA on the remaining 19 subjects extracted a BSA shape mode, which accounted for 24 % of the shape variability present in the population. 
52. This second “normalised” PLS regression yielded the EF shape mode, which accounted for 19 % of the remaining shape variability. 
53. Two subjects, who most likely contributed to the relatively weak correlation between EF and the EF shape vector, were subjects CoA5 and CoA15. 
54. This is why shape features associated with size differences are likely to be picked up by traditional 2D and 3D measurements. 
55. Therefore, the presented method can be used as a research tool to explore a population of 3D shapes, in order to detect where crucial shape changes occur and whether specific geometric parameters are likely to be of functional relevance. 
56. Overall employment of radiologic and MRI technologists is projected to grow 9 percent from 2020 to 2030, about as fast as the average for all occupations. 
57. For instance, in 2020, as per the Global Cancer Observatory, an interactive web based platform, it was reported that second most common cancer in Europe with estimated 477,534 newly diagnosed patients. 
58. For instance, according to the World Health Organization , in June 2021, it was observed that cardiovascular diseases are the leading cause of death across the globe. 
59. A. Asia Pacific is expected to register the highest CAGR of 7.6% from 2021 to 2030, owing to increase in number of diagnostic centers, and demand for advanced diagnosis of diseases. 
60. Squared errors between the estimated marker load and the true population marker load as well as cluster detection F1 scores are shown for each simulation experiment and for each marker load estimation technique.3.3. 
61. The two detected clusters occupy 1.8% of the volume of the cerebral cortex and 7.1% of the volume of the hippocampal region.3.4. 
62. The Aβ plaque number was significantly reduced in the cortical cluster in APPCreADAM30 mice . 
63. It remained similar in the hippocampal cluster for both groups . 
64. Notably, voxel based analysis detected clusters that span over 1.8% of the cerebral cortex and 7.1% of the hippocampal region. 
65. Squared errors between the estimated marker load and the true population marker load as well as cluster detection F1 scores are shown for each simulation experiment and for each marker load estimation technique. 
66. The two detected clusters occupy 1.8% of the volume of the cerebral cortex and 7.1% of the volume of the hippocampal region. 
67. The Aβ plaque number was significantly reduced in the cortical cluster in APPCre ADAM30 mice . 
68. The predicted results could be download without affecting any other parameters or results in the model . 
69. Radiation exposure from medical imaging in 2006 made up about 50% of total ionizing radiation exposure in the United States. 
70. In this case, all structures that have a tstatistic of at least 6.172145 or at most 6.172145 are significant at a 5% FDR threshold, all structures that have a tstatistic of at least 4.542356 or at most 4.542356 are significant at a 10% FDR threshold, etc. 
71. You can examine more than 350 causes in both adjusted and pre adjusted numbers, rates, and percentages for 204 countries and territories. 

These medical 3D visualization statistics provide a comprehensive look at the current state of this cutting-edge technology. From the increasing use of medical 3D visualization in medical specialties to the growing adoption of medical 3D visualization in the pharmaceutical and biotechnology industries, it’s clear that this technology is becoming an increasingly important tool for medical professionals. As technology continues to evolve and new medical 3D visualization techniques are developed, it is likely that this technology will continue to play an important role in improving patient outcomes and advancing the field of medicine.