Saturday 6 April 2024

The Intersection of AI and Radiology: A Look into the Future


Radiology has long been an essential component of modern healthcare, providing vital insights into the human body through imaging techniques such as X-rays, MRIs, and CT scans. With advancements in technology, particularly in artificial intelligence (AI), the field of radiology is experiencing a profound transformation. In this blog post, we will explore how AI is revolutionizing radiology software and shaping the future of diagnostic imaging.

By Emma Sturgis

Enhanced Image Analysis

One of the most significant contributions of AI to radiology software is its ability to improve image analysis. Traditional methods of interpreting medical images rely on human expertise, which can be time-consuming and prone to errors. AI algorithms, on the other hand, can process vast amounts of data quickly and accurately, leading to more precise and efficient diagnoses. By leveraging machine learning and deep learning techniques, AI-powered radiology software can detect subtle abnormalities that may be missed by human eyes, ultimately enhancing diagnostic accuracy.


Automated Workflow Optimization

AI algorithms integrated into radiology information systems have the potential to streamline workflow processes within healthcare facilities. Tasks such as image acquisition, processing, and interpretation can be automated, allowing radiologists to focus their time and expertise on complex cases that require human judgment. This automation not only improves efficiency but also reduces the likelihood of human error, resulting in faster diagnosis and treatment for patients.


Personalized Treatment Planning

Another area where AI is making a significant impact in radiology is personalized treatment planning. By analyzing imaging data alongside patient-specific information such as medical history and genetic markers, AI algorithms can help healthcare providers tailor treatment plans to individual patients' needs. This personalized approach not only enhances the effectiveness of treatments but also minimizes potential side effects, leading to better outcomes for patients.


Predictive Analytics

AI-powered radiology software is also enabling predictive analytics in healthcare by forecasting disease progression and treatment response. By analyzing historical patient data and imaging studies, AI algorithms can identify patterns and trends that may indicate future health outcomes. This capability allows healthcare providers to proactively intervene and customize treatment strategies based on predictive insights, ultimately improving patient care and outcomes.


Integration with Electronic Health Records

AI-driven radiology information systems are facilitating seamless integration with electronic health records (EHRs), creating a unified system for storing and accessing patient information. This interoperability enables healthcare providers to access relevant imaging data alongside clinical notes, lab results, and other critical information in real time. By having a comprehensive view of a patient's health record, providers can make more informed decisions regarding diagnosis, treatment, and follow-up care.


In conclusion, the intersection of AI and radiology represents a promising frontier in healthcare technology. By harnessing the power of artificial intelligence in radiology software, healthcare business managers can realize significant benefits such as enhanced image analysis, automated workflow optimization, personalized treatment planning, predictive analytics, and seamless integration with EHRs. As AI continues to advance, the future of radiology holds immense potential for improving diagnostic accuracy, patient outcomes, and overall quality of care.


Posted by Dr. Tim Sandle, Pharmaceutical Microbiology Resources (

Wednesday 3 April 2024

Introducing the Burkholderia cepacia complex

Image: CDC/Janice CarrContent Providers: Public Health Image Library (PHIL). Public Domain,

Members of the Burkholderia cepacia complex (BCC), of which there are 18 different species, which are grouped into nine genomovars. These are aerobic organisms, widely distributed, and found in soil and water[i]. Importantly they can additionally survive for long periods in low-nutrient moist environments[ii], which make these organisms probable survivors within pharmaceutical grade water systems.

By Tim Sandle

B. cepacia is a human opportunistic pathogen and can cause pneumonia in immunocompromised individuals (when introduced into the air passages of a susceptible population); other risks to patients include endocarditis, wound infections, intravenous bacteremia, foot infection, respiratory infections. Some patient groups are at a greater risk than others, including elderly people, young children, cancer patients, pregnant women, and people with chronic illness[iii].


Bcc is of concern in relation to many pharmaceutical and healthcare facilities because many of the organisms within the group are resistant to organic solvents and antiseptics, and, to a degree, certain disinfectants[iv], with the resistance arising from several factors, including efflux pump mechanisms and resistance conferred through the organisms having a tendency to form biofilms under optimal conditions. Bcc organisms are also persistent, and they can readily survive in low nutrient conditions (such purified or distilled water).


It is important to understand the potential points or origin in pharmaceutical facilities (which is primarily low-nutrient environs like water, with the organisms adept at surviving under low nutrient conditions[v] [vi]; and which are reflective of the organisms often being able to adapt to different environmental conditions[vii]).


Organism characteristics


Burkholderia is a genus composed of over 60 organisms, many of which were formerly classed as Pseudomonas species. Within this are the Burkholderia cepacia complex, a group of some 17 organisms which are so closely related that they can, for the most part, only be differentiated by using a combination of multiple molecular diagnostic procedures.


Members of the Burkholderia cepacia complex are Gram-negative bacteria of the β-proteobacteria subdivision. This group is composed of plant, animal, and human pathogens. The organisms are widespread in both natural and ‘as built’ habitats[viii]. The organism after which the group is named was known as Pseudomonas cepacia prior to 1992. The bacterium was discovered by Walter H. Burkholder at Cornell Universityin1947.  Burkholder identified the bacterium as the source of onion skin rot (cepacia is Latin for “like onion”).


Burkholderia cepacia, along with other members, is an aerobic bacterium, elliptically shaped with a length of 5–15 μm. In term of biohazard, the organism has a biosafety level of 2.


Origins in pharmaceutical and healthcare


Bcc organisms are common to the environment and to water[ix].  With the manufacturing of drug products, the most common point of origin is with pharmaceutical water systems; a review by Sandle (2015) indicated that organisms fall into the top five category of recovered water-borne contaminants, as assessed over a fifteen year period[x]. This related to recoveries of water microbiota from purified water and Water-for-Injections systems. Issues arise foremost due to deficiencies in the design, operation and monitoring of water systems. A key risk relates to maintenance work like valve changes or where the system requires ‘cutting into’, such as to alter pipework[xi].

[i] Lipuma J.J.. Update on the Burkholderia cepacia complex, Curr Opin Pulm Med. 2005; 11(6): 528-33

[ii] Lipuma, J.J, Currie B.J, Lum G.D, and Vandamme P. Burkholderia, Stenotrophomonas, Ralstonia, Cupriavidus, Pandoraea, Brevundimonas, Comamonas, Delftia, and Acidovorax In: Murray P.R, Baron E J, Jorgensen J.H, Landry ML, and Pfaller MA, editors. Manual of Clinical Microbiology. 9th Ed. Washington DC: ASM Press; 2007. p. 749-769.

[iii] Torbeck L, D. Raccasi, D.E. Guilfoyle, R.L. Friedman, D. Hussong. 2011. Burkholderia cepacia: This Decision is Overdue. PDA J. Pharm. Sci. Tech., 65(5): 535-43.

[iv] Hugo, WB et al. 1986. Factors Contributing to the Survival of a Strain of Pseudomonas cepacia In Chlorhexidine Solutions. Lett Appl Microbiol. 2:37-42

[v] W. Beckman and T.G. Lessie. Response of Pseudomonas cepacia to p-lactam antibiotics: utilization of penicillin G as the carbon source. J. Bacteriol. 1979; 140: 1126-1128

[vi] Martin, M et al 2011. Hospital-wide outbreak of Burkholderia contaminans caused by prefabricated moist washcloths. J Hosp Infect 77:267-270

[vii] Vial, L., et al 2011. The various lifestyles of the Burkholderia cepacia complex species: a tribute to adaptation. Envir Microb 13(1):1-12

[viii] E. Mahenthiralingam, T.A. Urban, and J.B. Goldberg. The multifarious, multireplicon Burkholderia cepacia complex. Nature Reviews Microbiol. 2005; 3(2): 144–156

[ix] Springman, A.; Jacobs, J. L.; Somvanshi, V. S.; Sundin, G. W.; Mulks, M. H.; Whittam, T. S.; Viswanathan, P.; Gray, R. L.; Lipuma, J. J.; Ciche, T. A. Genetic diversity and multihost pathogenicity of clinical and environmental strains of Burkholderia cenocepacia. Appl. Environ. Microbiol. 2009, 75 (16), 5250–5260

[x] Sandle T (2015) Characterizing the Microbiota of a Pharmaceutical Water System-A Metadata Study. SOJ Microbiol Infect Page 5 of 8 Dis 3(2): 1-8

[xi] Ali, M. (2016) Burkholderia Cepacia in Pharmaceutical Industries, Int J Vaccines Vaccin 3(2): 00064. DOI: 10.15406/ijvv.2016.03.00064

Posted by Dr. Tim Sandle, Pharmaceutical Microbiology Resources (

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