Toward Clinically Relevant Standardization of Image Quality

Samei, Ehsan; Rowberg, Alan H.; Avraham, Ellie; Cornelius, Craig

doi:10.1007/s10278-004-1031-5

Cited by 12 publications

(7 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…2001). As increasing demands on better image quality in endodontic practice, there is a growing need to measure and document image quality at all steps from acquisition through display where efficiency and reliability are particularly important (Samei et al. 2004).…”

Section: Introductionmentioning

confidence: 99%

“…Careful assessment of the root canal system based on high quality radiography is a prerequisite for all stages of root canal treatment from initial diagnosis through the monitoring of treatment (Lavelle 1999, Wallace et al 2001. As increasing demands on better image quality in endodontic practice, there is a growing need to measure and document image quality at all steps from acquisition through display where efficiency and reliability are particularly important (Samei et al 2004).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Imaging of root canal fillings: a comparison of subjective image quality between limited cone‐beam CT, storage phosphor and film radiography

2007

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Imaging of root canal fillings: a comparison of subjective image quality between limited cone‐beam CT, storage phosphor and film radiography

2007

View full text Add to dashboard Cite

show abstract

“…8 Compared to natural images, medical images have regulated quality that can reduce noise and therefore make them more useful for deep learning-based approaches. 9 However, while medical images can be an ideal source for deep learning, it remains difficult to secure a large quantity of clinically annotated datasets. 10 Since classification accuracy is dependent on the size of the initial training datasets, computational methods that seek to optimize model performance are critical.…”

Section: Introductionmentioning

confidence: 99%

An image-based deep learning framework for individualising radiotherapy dose: a retrospective analysis of outcome prediction

Lou

Doken²,

Zhuang

et al. 2019

The Lancet Digital Health

173

110

View full text Add to dashboard Cite

Background: Radiotherapy continues to be delivered uniformly without consideration of individual tumor characteristics. To advance toward more precise treatments in radiotherapy, we queried the lung computed tomography (CT)-derived feature space to identify radiation sensitivity parameters that can predict treatment failure and hence guide the individualization of radiotherapy dose. Methods: We used a cohort-based registry of 849 patients with cancer in the lung treated with high dose radiotherapy using stereotactic body radiotherapy. We input pre-therapy lung CT images into a multi-task deep neural network, Deep Profiler, to generate an image fingerprint that primarily predicts time to event treatment outcomes and secondarily approximates classical radiomic features. We validated our findings in an independent study population (n = 95). Deep Profiler was combined with clinical variables to derive iGray, an individualized dose that estimates treatment failure probability to be <5%. Findings: Radiation treatments in patients with high Deep Profiler scores fail at a significantly higher rate than in those with low scores. The 3-year cumulative incidences of local failure were 20.3% (95% CI: 16.0–24.9) and 5.7% (95% CI: 3.5–8.8), respectively. Deep Profiler independently predicted local failure (hazard ratio 1.65, 95% 1.02–2.66, p = 0.04). Models that included Deep Profiler and clinical variables predicted treatment failures with a concordance index of 0.72 (95% CI: 0.67–0.77), a significant improvement compared to classical radiomics or clinical variables alone (p = <0.001 and <0.001, respectively). Deep Profiler performed well in an external study population (n = 95), accurately predicting treatment failures across diverse clinical settings and CT scanner types (concordance index = 0.77 [95% CI: 0.69–0.92]). iGray had a wide dose range (21.1–277 Gy, BED), suggested dose reductions in 23.3% of patients and can be safely delivered in the majority of cases. Interpretation: Our results indicate that there are image-distinct subpopulations that have differential sensitivity to radiotherapy. The image-based deep learning framework proposed herein is the first opportunity to use medical images to individualize radiotherapy dose.

show abstract

“…For medical image standardization, the Digital Imaging and Communications in Medicine (DICOM) Standard [34] is a framework developed to allow for easy image storage and exchange for medical images from diverse vendors. The DICOM Grayscale Standard Display Function (GSDF) has been shown to increase visual consistency across medical images, but only improves the luminance response, which is just one of many factors that influence the quality of a medical image, including reflection, spatial resolution, noise, geometrical distortions, display chromaticity, veiling glare, and temporal response [35]. Thus, DICOM GSDF is useful for medical image standardization but not the most extensive solution.…”

Section: A Classical Approaches For Handling Unstandardized Datamentioning

confidence: 99%

Systematic Review of Advanced AI Methods for Improving Healthcare Data Quality in Post COVID-19 Era

Isgut

Gloster

Choi

et al. 2023

IEEE Rev. Biomed. Eng.

View full text Add to dashboard Cite

At the beginning of the COVID-19 pandemic, there was significant hype about the potential impact of artificial intelligence (AI) tools in combatting COVID-19 on diagnosis, prognosis, or surveillance. However, AI tools have not yet been widely successful. One of the key reason is the COVID-19 pandemic has demanded faster real-time development of AI-driven clinical and health support tools, including rapid data collection, algorithm development, validation, and deployment. However, there was not enough time for proper data quality control. Learning from the hard lessons in COVID-19, we summarize the important health data quality challenges during COVID-19 pandemic such as lack of data standardization, missing data, tabulation errors, and noise and artifact. Then we conduct a systematic investigation of computational methods that address these issues, including emerging novel advanced AI data quality control methods that achieve better data quality outcomes and, in some cases, simplify or automate the data cleaning process. We hope this article can assist healthcare community to improve health data quality going forward with novel AI development.

show abstract

Toward Clinically Relevant Standardization of Image Quality

Cited by 12 publications

References 1 publication

Imaging of root canal fillings: a comparison of subjective image quality between limited cone‐beam CT, storage phosphor and film radiography

Imaging of root canal fillings: a comparison of subjective image quality between limited cone‐beam CT, storage phosphor and film radiography

An image-based deep learning framework for individualising radiotherapy dose: a retrospective analysis of outcome prediction

Systematic Review of Advanced AI Methods for Improving Healthcare Data Quality in Post COVID-19 Era

Contact Info

Product

Resources

About