83 results on '"Marc D, Kohli"'
Search Results
2. Theory of radiologist interaction with instant messaging decision support tools: A sequential-explanatory study.
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John Lee Burns, Judy Wawira Gichoya, Marc D Kohli, Josette Jones, and Saptarshi Purkayastha
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Computer applications to medicine. Medical informatics ,R858-859.7 - Abstract
Radiology specific clinical decision support systems (CDSS) and artificial intelligence are poorly integrated into the radiologist workflow. Current research and development efforts of radiology CDSS focus on 4 main interventions, based around exam centric time points-after image acquisition, intra-report support, post-report analysis, and radiology workflow adjacent. We review the literature surrounding CDSS tools in these time points, requirements for CDSS workflow augmentation, and technologies that support clinician to computer workflow augmentation. We develop a theory of radiologist-decision tool interaction using a sequential explanatory study design. The study consists of 2 phases, the first a quantitative survey and the second a qualitative interview study. The phase 1 survey identifies differences between average users and radiologist users in software interventions using the User Acceptance of Information Technology: Toward a Unified View (UTAUT) framework. Phase 2 semi-structured interviews provide narratives on why these differences are found. To build this theory, we propose a novel solution called Radibot-a conversational agent capable of engaging clinicians with CDSS as an assistant using existing instant messaging systems supporting hospital communications. This work contributes an understanding of how radiologist-users differ from the average user and can be utilized by software developers to increase satisfaction of CDSS tools within radiology. more...
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- 2024
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3. Multi-institutional Experience with Patient Image Access Through Electronic Health Record Patient Portals.
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Hailey H. Choi, Amy L. Kotsenas, Joshua Vic Chen, Christina Bronsky, Christopher J. Roth, and Marc D. Kohli
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- 2022
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4. Impact of patient portal-based self-scheduling of diagnostic imaging studies on health disparities.
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Smitha Ganeshan, Logan Pierce, Michelle Mourad, Timothy J. Judson, Marc D. Kohli, Anobel Y. Odisho, and William Brown
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- 2022
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5. Assessment of an ensemble of machine learning models toward abnormality detection in chest radiographs.
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Sivaramakrishnan Rajaraman, Sudhir Sornapudi, Marc D. Kohli, and Sameer K. Antani
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- 2019
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6. Between Always and Never: Evaluating Uncertainty in Radiology Reports Using Natural Language Processing.
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Andrew L. Callen, Sara M. Dupont, Adi Price, Ben Laguna, David McCoy, Bao Do, Jason Talbott, Marc D. Kohli, and Jared Narvid
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- 2020
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7. A novel stacked generalization of models for improved TB detection in chest radiographs.
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Sivaramakrishnan Rajaraman, Sema Candemir, Zhiyun Xue, Philip O. Alderson, Marc D. Kohli, Joseph Abuya, George R. Thoma, and Sameer K. Antani
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- 2018
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8. The Evidence for Using Artificial Intelligence to Enhance Prostate Cancer MR Imaging
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Rodrigo Canellas, Marc D. Kohli, and Antonio C. Westphalen
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Oncology - Published
- 2023
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9. Three cachexia phenotypes and the impact of fat‐only loss on survival in FOLFIRINOX therapy for pancreatic cancer
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Joshua K. Kays, Safi Shahda, Melissa Stanley, Teresa M. Bell, Bert H. O'Neill, Marc D. Kohli, Marion E. Couch, Leonidas G. Koniaris, and Teresa A. Zimmers
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Cachexia ,Pancreatic cancer ,Sarcopenia ,FOLFIRINOX ,Muscle wasting ,Diseases of the musculoskeletal system ,RC925-935 ,Human anatomy ,QM1-695 - Abstract
Abstract Background By the traditional definition of unintended weight loss, cachexia develops in ~80% of patients with pancreatic ductal adenocarcinoma (PDAC). Here, we measure the longitudinal body composition changes in patients with advanced PDAC undergoing 5‐fluorouracil, leucovorin, irinotecan, and oxaliplatin therapy. Methods We performed a retrospective review of 53 patients with advanced PDAC on 5‐fluorouracil, leucovorin, irinotecan, and oxaliplatin as first line therapy at Indiana University Hospital from July 2010 to August 2015. Demographic, clinical, and survival data were collected. Body composition measurement by computed tomography (CT), trend, univariate, and multivariate analysis were performed. Results Among all patients, three cachexia phenotypes were identified. The majority of patients, 64%, had Muscle and Fat Wasting (MFW), while 17% had Fat‐Only Wasting (FW) and 19% had No Wasting (NW). NW had significantly improved overall median survival (OMS) of 22.6 months vs. 13.0 months for FW and 12.2 months for MFW (P = 0.02). FW (HR = 5.2; 95% confidence interval = 1.5–17.3) and MFW (HR = 1.8; 95% confidence interval = 1.1–2.9) were associated with an increased risk of mortality compared with NW. OMS and risk of mortality did not differ between FW and MFW. Progression of disease, sarcopenic obesity at diagnosis, and primary tail tumours were also associated with decreased OMS. On multivariate analysis, cachexia phenotype and chemotherapy response were independently associated with survival. Notably, CT‐based body composition analysis detected tissue loss of >5% in 81% of patients, while the traditional definition of >5% body weight loss identified 56.6%. Conclusions Distinct cachexia phenotypes were observed in this homogeneous population of patients with equivalent stage, diagnosis, and first‐line treatment. This suggests cellular, molecular, or genetic heterogeneity of host or tumour. Survival among patients with FW was as poor as for MFW, indicating adipose tissue plays a crucial role in cachexia and PDAC mortality. Adipose tissue should be studied for its mechanistic contributions to cachexia. more...
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- 2018
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10. A Platform for Innovation and Standards Evaluation: a Case Study from the OpenMRS Open-Source Radiology Information System.
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Judy W. Gichoya, Marc D. Kohli, Larry Ivange, Teri Sippel Schmidt, and Saptarshi Purkayastha
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- 2018
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11. Creation and Curation of the Society of Imaging Informatics in Medicine Hackathon Dataset.
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Marc D. Kohli, James J. Morrison, Judy Wawira Gichoya, Matthew B. Morgan, Jason Hostetter, Brad W. Genereaux, Mohannad Hussain, and Steve G. Langer
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- 2018
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12. Learning HL7 FHIR Using the HAPI FHIR Server and Its Use in Medical Imaging with the SIIM Dataset.
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Mohannad Hussain, Steve G. Langer, and Marc D. Kohli
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- 2018
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13. Proving Value in Radiology: Experience Developing and Implementing a Shareable Open Source Registry Platform Driven by Radiology Workflow.
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Judy Wawira Gichoya, Marc D. Kohli, Paul Haste, Elizabeth Mills Abigail, and Matthew Johnson
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- 2017
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14. Medical Image Data and Datasets in the Era of Machine Learning - Whitepaper from the 2016 C-MIMI Meeting Dataset Session.
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Marc D. Kohli, Ronald M. Summers, and J. Raymond Geis
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- 2017
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15. An Image Quality-informed Framework for CT Characterization
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Jason Luong, Rebecca Smith-Bindman, Biraj Bista, J. Anthony Seibert, Philip W. Chu, Bradley N. Delman, Sophronia Yu, Carly Stewart, Andrew J. Einstein, Alejandro Alejandrez Cisneros, Yifei Wang, Andrew B. Bindman, Patrick S Romano, Antonio C. Westphalen, Denise Bos, Michael J. Flynn, Robert Chung, and Marc D. Kohli more...
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Adult ,Male ,Metadata ,business.industry ,Image quality ,Radiation dose ,Medizin ,Benchmarking ,Middle Aged ,computer.software_genre ,Radiation Dosage ,Radiographic Image Interpretation, Computer-Assisted ,Medicine ,Humans ,Radiology, Nuclear Medicine and imaging ,Body region ,Female ,Data mining ,business ,Tomography, X-Ray Computed ,computer ,Original Research ,Aged ,Retrospective Studies - Abstract
BACKGROUND: Lack of standardization in CT protocol choice contributes to radiation dose variation. PURPOSE: To create a framework to assess radiation doses within broad CT categories defined according to body region and clinical imaging indication and to cluster indications according to the dose required for sufficient image quality. MATERIALS AND METHODS: This was a retrospective study using Digital Imaging and Communications in Medicine metadata. CT examinations in adults from January 1, 2016 to December 31, 2019 from the University of California San Francisco International CT Dose Registry were grouped into 19 categories according to body region and required radiation dose levels. Five body regions had a single dose range (ie, extremities, neck, thoracolumbar spine, combined chest and abdomen, and combined thoracolumbar spine). Five additional regions were subdivided according to dose. Head, chest, cardiac, and abdomen each had low, routine, and high dose categories; combined head and neck had routine and high dose categories. For each category, the median and 75th percentile (ie, diagnostic reference level [DRL]) were determined for dose-length product, and the variation in dose within categories versus across categories was calculated and compared using an analysis of variance. Relative median and DRL (95% CI) doses comparing high dose versus low dose categories were calculated. RESULTS: Among 4.5 million examinations, the median and DRL doses varied approximately 10 times between categories compared with between indications within categories. For head, chest, abdomen, and cardiac (3 266 546 examinations [72%]), the relative median doses were higher in examinations assigned to the high dose categories than in examinations assigned to the low dose categories, suggesting the assignment of indications to the broad categories is valid (head, 3.4-fold higher [95% CI: 3.4, 3.5]; chest, 9.6 [95% CI: 9.3, 10.0]; abdomen, 2.4 [95% CI: 2.4, 2.5]; and cardiac, 18.1 [95% CI: 17.7, 18.6]). Results were similar for DRL doses (all P < .001). CONCLUSION: Broad categories based on image quality requirements are a suitable framework for simplifying radiation dose assessment, according to expected variation between and within categories. © RSNA, 2021 See also the editorial by Mahesh in this issue. more...
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- 2023
16. Automated detection of IVC filters on radiographs with deep convolutional neural networks
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John Mongan, Marc D. Kohli, Roozbeh Houshyar, Peter D. Chang, Justin Glavis-Bloom, and Andrew G. Taylor
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Artificial intelligence ,Vena Cava Filters ,Radiological and Ultrasound Technology ,Neural Networks ,Urology ,Gastroenterology ,Deep learning ,Radiography ,Computer ,Screening ,Humans ,Radiology, Nuclear Medicine and imaging ,Inferior vena cava filter ,Algorithms ,Retrospective Studies - Abstract
Purpose To create an algorithm able to accurately detect IVC filters on radiographs without human assistance, capable of being used to screen radiographs to identify patients needing IVC filter retrieval. Methods A primary dataset of 5225 images, 30% of which included IVC filters, was assembled and annotated. 85% of the data was used to train a Cascade R-CNN (Region Based Convolutional Neural Network) object detection network incorporating a pre-trained ResNet-50 backbone. The remaining 15% of the data, independently annotated by three radiologists, was used as a test set to assess performance. The algorithm was also assessed on an independently constructed 1424-image dataset, drawn from a different institution than the primary dataset. Results On the primary test set, the algorithm achieved a sensitivity of 96.2% (95% CI 92.7–98.1%) and a specificity of 98.9% (95% CI 97.4–99.5%). Results were similar on the external test set: sensitivity 97.9% (95% CI 96.2–98.9%), specificity 99.6 (95% CI 98.9–99.9%). Conclusion Fully automated detection of IVC filters on radiographs with high sensitivity and excellent specificity required for an automated screening system can be achieved using object detection neural networks. Further work will develop a system for identifying patients for IVC filter retrieval based on this algorithm. Graphical abstract more...
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- 2023
17. A Practice Quality Improvement Project: Reducing Dose of Routine Chest CT Imaging in a Busy Clinical Practice.
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Edwin A. Takahashi, Marc D. Kohli, and Shawn D. Teague
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- 2016
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18. Toward Data-Driven Radiology Education - Early Experience Building Multi-Institutional Academic Trainee Interpretation Log Database (MATILDA).
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Po-Hao Chen, Thomas W. Loehfelm, Aaron P. Kamer, Andrew B. Lemmon, Tessa Sundaram Cook, and Marc D. Kohli
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- 2016
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19. Radiology Quality Measure Compliance Reporting: an Automated Approach.
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Marc D. Kohli and Duane Schonlau
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- 2016
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20. Preparing a collection of radiology examinations for distribution and retrieval.
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Dina Demner-Fushman, Marc D. Kohli, Marc B. Rosenman, Sonya E. Shooshan, Laritza Rodriguez, Sameer K. Antani, George R. Thoma, and Clement J. McDonald
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- 2016
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21. Automated detection of IVC filters on radiographs with deep convolutional neural networks
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John, Mongan, Marc D, Kohli, Roozbeh, Houshyar, Peter D, Chang, Justin, Glavis-Bloom, and Andrew G, Taylor
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To create an algorithm able to accurately detect IVC filters on radiographs without human assistance, capable of being used to screen radiographs to identify patients needing IVC filter retrieval.A primary dataset of 5225 images, 30% of which included IVC filters, was assembled and annotated. 85% of the data was used to train a Cascade R-CNN (Region Based Convolutional Neural Network) object detection network incorporating a pre-trained ResNet-50 backbone. The remaining 15% of the data, independently annotated by three radiologists, was used as a test set to assess performance. The algorithm was also assessed on an independently constructed 1424-image dataset, drawn from a different institution than the primary dataset.On the primary test set, the algorithm achieved a sensitivity of 96.2% (95% CI 92.7-98.1%) and a specificity of 98.9% (95% CI 97.4-99.5%). Results were similar on the external test set: sensitivity 97.9% (95% CI 96.2-98.9%), specificity 99.6 (95% CI 98.9-99.9%).Fully automated detection of IVC filters on radiographs with high sensitivity and excellent specificity required for an automated screening system can be achieved using object detection neural networks. Further work will develop a system for identifying patients for IVC filter retrieval based on this algorithm. more...
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- 2022
22. Building Blocks for a Clinical Imaging Informatics Environment.
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Marc D. Kohli, Max Warnock, Mark Daly, Christopher Toland, Christopher Meenan, and Paul G. Nagy
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- 2014
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23. Comparing deep learning models for population screening using chest radiography.
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Sivaramakrishnan Rajaraman, Sameer K. Antani, Sema Candemir, Zhiyun Xue, Joseph Abuya, Marc D. Kohli, Philip O. Alderson, and George R. Thoma
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- 2018
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24. What is a Wiki, and How Can it be Used in Resident Education?
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Marc D. Kohli and John K. Bradshaw
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- 2011
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25. Between Always and Never: Evaluating Uncertainty in Radiology Reports Using Natural Language Processing
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Sara M. Dupont, Jared Narvid, Adi Price, Jason F. Talbott, Andrew L. Callen, Bao H. Do, David McCoy, Marc D. Kohli, and Ben Laguna
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Research Report ,medicine.medical_specialty ,Computer science ,media_common.quotation_subject ,Patient characteristics ,computer.software_genre ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,0302 clinical medicine ,medicine ,Humans ,Radiology, Nuclear Medicine and imaging ,Single institution ,Set (psychology) ,Natural Language Processing ,media_common ,Original Paper ,Modalities ,Radiological and Ultrasound Technology ,business.industry ,Uncertainty ,Gold standard (test) ,Ambiguity ,Computer Science Applications ,Test (assessment) ,Radiology report ,Radiology Information Systems ,Artificial intelligence ,Radiology ,business ,computer ,030217 neurology & neurosurgery ,Natural language processing - Abstract
The ideal radiology report reduces diagnostic uncertainty, while avoiding ambiguity whenever possible. The purpose of this study was to characterize the use of uncertainty terms in radiology reports at a single institution and compare the use of these terms across imaging modalities, anatomic sections, patient characteristics, and radiologist characteristics. We hypothesized that there would be variability among radiologists and between subspecialities within radiology regarding the use of uncertainty terms and that the length of the impression of a report would be a predictor of use of uncertainty terms. Finally, we hypothesized that use of uncertainty terms would often be interpreted by human readers as “hedging.” To test these hypotheses, we applied a natural language processing (NLP) algorithm to assess and count the number of uncertainty terms within radiology reports. An algorithm was created to detect usage of a published set of uncertainty terms. All 642,569 radiology report impressions from 171 reporting radiologists were collected from 2011 through 2015. For validation, two radiologists without knowledge of the software algorithm reviewed report impressions and were asked to determine whether the report was “uncertain” or “hedging.” The relationship between the presence of 1 or more uncertainty terms and the human readers’ assessment was compared. There were significant differences in the proportion of reports containing uncertainty terms across patient admission status and across anatomic imaging subsections. Reports with uncertainty were significantly longer than those without, although report length was not significantly different between subspecialities or modalities. There were no significant differences in rates of uncertainty when comparing the experience of the attending radiologist. When compared with reader 1 as a gold standard, accuracy was 0.91, sensitivity was 0.92, specificity was 0.9, and precision was 0.88, with an F1-score of 0.9. When compared with reader 2, accuracy was 0.84, sensitivity was 0.88, specificity was 0.82, and precision was 0.68, with an F1-score of 0.77. Substantial variability exists among radiologists and subspecialities regarding the use of uncertainty terms, and this variability cannot be explained by years of radiologist experience or differences in proportions of specific modalities. Furthermore, detection of uncertainty terms demonstrates good test characteristics for predicting human readers’ assessment of uncertainty. more...
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- 2020
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26. Implementation and design of artificial intelligence in abdominal imaging
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Silvia D. Chang, Marc D. Kohli, and Hailey H. Choi
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Radiological and Ultrasound Technology ,business.industry ,Urology ,Deep learning ,Gastroenterology ,medicine.disease ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,Prostate cancer ,0302 clinical medicine ,Section (archaeology) ,030220 oncology & carcinogenesis ,medicine ,Radiology, Nuclear Medicine and imaging ,Artificial intelligence ,business - Abstract
Artificial intelligence is a technique that holds promise for helping radiologists improve the care of our patients. At the same time, implementation decisions we make now can have a long-lasting effect on patient outcomes. In the following article, we discuss four areas with unique considerations for implementation of AI: bias, trust, risk, and design. In each section, we highlight applications of AI to abdominal imaging and prostate cancer specifically. more...
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- 2020
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27. Multi-institutional Experience with Patient Image Access Through Electronic Health Record Patient Portals
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Hailey H. Choi, Amy L. Kotsenas, Joshua Vic Chen, Christina Bronsky, Christopher J. Roth, and Marc D. Kohli
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Radiological and Ultrasound Technology ,Prevention ,Clinical Sciences ,Image sharing ,Healthcare transparency ,Information technology ,Article ,Computer Science Applications ,Nuclear Medicine & Medical Imaging ,Good Health and Well Being ,Patient Portals ,Clinical Research ,Patient image access ,Radiologists ,Biomedical Imaging ,Electronic Health Records ,Humans ,Radiology, Nuclear Medicine and imaging ,Radiology ,Cures Act ,Radiology workflow ,Retrospective Studies - Abstract
The objective is to determine patients’ utilization rate of radiology image viewing through an online patient portal and to understand its impact on radiologists. IRB approval was waived. In this two-part, multi-institutional study, patients’ image viewing rate was retrospectively assessed, and radiologists were anonymously surveyed for the impact of patient imaging access on their workflow. Patient access to web-based image viewing via electronic patient portals was enabled at 3 institutions (all had open radiology reports) within the past 5 years. The number of exams viewed online was compared against the total number of viewable imaging studies. An anonymized survey was distributed to radiologists at the 3 institutions, and responses were collected over 2 months. Patients viewed 14.2% of available exams – monthly open rate varied from 7.3 to 41.0%. A total of 254 radiologists responded to the survey (response rate 32.8%); 204 were aware that patients could view images. The majority (155/204; 76.0%) felt no impact on their role as radiologists; 11.8% felt negative and 9.3% positive. The majority (63.8%) were never approached by patients. Of the 86 who were contacted, 46.5% were contacted once or twice, 46.5% 3–4 times a year, and 4.7% 3–4 times a month. Free text comments included support for healthcare transparency (71), concern for patient confusion and anxiety (45), and need for attention to radiology reports and image annotations (15). A small proportion of patients viewed their radiology images. Overall, patients’ image viewing had minimal impact on radiologists. Radiologists were seldom contacted by patients. While many radiologists feel supportive, some are concerned about causing patient confusion and suggest minor workflow modifications. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10278-021-00565-9. more...
- Published
- 2021
28. Developing a Radiology Information System and Picture Archiving and Communications System (RIS/PACS) for a Kenyan Hospital.
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Marc D. Kohli, Donald Hawes, Burke W. Mamlin, Paul G. Biondich, and Matthew Johnson
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- 2006
29. Single institutional experience with initial ultrasound followed by computed tomography or magnetic resonance imaging for acute appendicitis in adults
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Liina Poder, Priyanka Jha, Nora Espinoza, Emily M. Webb, Tara A. Morgan, and Marc D. Kohli
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Adult ,Male ,medicine.medical_specialty ,Adolescent ,Urology ,Appendix ,030218 nuclear medicine & medical imaging ,Diagnosis, Differential ,Young Adult ,03 medical and health sciences ,0302 clinical medicine ,Internal medicine ,medicine ,Humans ,Radiology, Nuclear Medicine and imaging ,Aged ,Retrospective Studies ,Ultrasonography ,Aged, 80 and over ,Radiological and Ultrasound Technology ,medicine.diagnostic_test ,business.industry ,Medical record ,Ultrasound ,Gastroenterology ,Magnetic resonance imaging ,Retrospective cohort study ,Middle Aged ,Hepatology ,Appendicitis ,medicine.disease ,Magnetic Resonance Imaging ,Triage ,medicine.anatomical_structure ,030220 oncology & carcinogenesis ,Acute Disease ,Female ,Radiology ,Tomography, X-Ray Computed ,business - Abstract
Purpose The objectives of this study was to assess the performance of ultrasound (US) for suspected appendicitis in adult patients and to evaluated the additive value of short-interval (within 1 week) computed tomography (CT) or Magnetic Resonance Imaging (MRI) after performing an initial US. Methods In this IRB-approved, HIPAA-compliant, retrospective study, electronic medical records (EMRs) were queried for "US appendicitis" performed over a 2-year interval. EMR was reviewed for CT or MRI performed within 1 week of this exam, and if any new or additional information was available at subsequent exam. White count, patient disposition, and pathology, if surgery was performed, were also recorded. Results 682 patients underwent US for appendicitis over a 2-year duration, age range from 18 to 92 years (average: 30.1 years, M:F = 141:541). Findings showed 126/682 patients with normal appendix, 75/682 uncomplicated appendicitis, and 4/682 with complicated appendicitis. When performed, no additional findings were seen in these groups on short-interval CT or MRI. 2/682 patients had equivocal findings on US but eventually had normal appendix identified on CT. Four hundred and seventy-three patients had non-visualized appendix, of which only 14/473 (3.1%) eventually had appendicitis. Conclusions Ultrasound is an effective initial modality for evaluating appendicitis even in adult patients. Once a normal appendix, uncomplicated or complicated appendicitis is identified on US, no further imaging is necessary. Very few patients with non-visualization of the appendix eventually have appendicitis. Hence, these patients can be managed with active clinical follow-up rather than immediate CT or MRI. Symptoms and clinical scoring systems can be used for triage of these patients. more...
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- 2019
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30. The RSNA Pediatric Bone Age Machine Learning Challenge
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Rafael Teixeira Sousa, Leon Chen, Alexander Bilbily, Marc D. Kohli, Hans Henrik Thodberg, Adam E. Flanders, George Shih, Mark Cicero, Bradley J. Erickson, Felipe Kitamura, Safwan Halabi, Ian Pan, Luciano M. Prevedello, Katherine P. Andriole, Nitamar Abdala, Jayashree Kalpathy-Cramer, Lucas Araújo Pereira, and Artem Mamonov more...
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Male ,Databases, Factual ,Machine learning ,computer.software_genre ,Medical and Health Sciences ,Convolutional neural network ,030218 nuclear medicine & medical imaging ,Machine Learning ,Databases ,03 medical and health sciences ,Computer-Assisted ,0302 clinical medicine ,Age Determination by Skeleton ,Image Interpretation, Computer-Assisted ,Medical imaging ,Humans ,Medicine ,Radiology, Nuclear Medicine and imaging ,Child ,Set (psychology) ,Image Interpretation ,Factual ,Artificial neural network ,business.industry ,Bone age ,Test (assessment) ,Radiography ,Data set ,Nuclear Medicine & Medical Imaging ,Hand Bones ,030220 oncology & carcinogenesis ,Test set ,Female ,Artificial intelligence ,business ,computer ,Algorithms - Abstract
Purpose The Radiological Society of North America (RSNA) Pediatric Bone Age Machine Learning Challenge was created to show an application of machine learning (ML) and artificial intelligence (AI) in medical imaging, promote collaboration to catalyze AI model creation, and identify innovators in medical imaging. Materials and Methods The goal of this challenge was to solicit individuals and teams to create an algorithm or model using ML techniques that would accurately determine skeletal age in a curated data set of pediatric hand radiographs. The primary evaluation measure was the mean absolute distance (MAD) in months, which was calculated as the mean of the absolute values of the difference between the model estimates and those of the reference standard, bone age. Results A data set consisting of 14 236 hand radiographs (12 611 training set, 1425 validation set, 200 test set) was made available to registered challenge participants. A total of 260 individuals or teams registered on the Challenge website. A total of 105 submissions were uploaded from 48 unique users during the training, validation, and test phases. Almost all methods used deep neural network techniques based on one or more convolutional neural networks (CNNs). The best five results based on MAD were 4.2, 4.4, 4.4, 4.5, and 4.5 months, respectively. Conclusion The RSNA Pediatric Bone Age Machine Learning Challenge showed how a coordinated approach to solving a medical imaging problem can be successfully conducted. Future ML challenges will catalyze collaboration and development of ML tools and methods that can potentially improve diagnostic accuracy and patient care. © RSNA, 2018 Online supplemental material is available for this article. See also the editorial by Siegel in this issue. more...
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- 2019
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31. Quality Comparison of 3 Tesla multiparametric MRI of the prostate using a flexible surface receiver coil versus conventional surface coil plus endorectal coil setup
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Antonio C. Westphalen, Kirti Magudia, Baris Turkbey, L. Schimmöller, Michael A. Ohliger, Marc D. Kohli, Leonardo Kayat Bittencourt, Tristan Barrett, Sandeep Arora, Daniel Margolis, T Ullrich, Barrett, Tristan [0000-0002-1180-1474], Apollo - University of Cambridge Repository, and Ullrich, T. [0000-0002-9866-8898] more...
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Male ,Image quality ,Urology ,Signal-To-Noise Ratio ,030218 nuclear medicine & medical imaging ,Pelvis ,Receiver coil ,03 medical and health sciences ,0302 clinical medicine ,Prostate ,medicine ,Surface coil ,Humans ,Radiology, Nuclear Medicine and imaging ,Multiparametric Magnetic Resonance Imaging ,3 Tesla Magnetic Resonance Imaging ,Prostate cancer ,Radiological and Ultrasound Technology ,business.industry ,Gastroenterology ,Prostatic Neoplasms ,Multiparametric MRI ,Early diagnosis ,Magnetic Resonance Imaging ,Quality ,medicine.anatomical_structure ,030220 oncology & carcinogenesis ,business ,Nuclear medicine ,Benign prostate ,Endorectal coil ,MRI - Abstract
Purpose To subjectively and quantitatively compare the quality of 3 Tesla magnetic resonance imaging of the prostate acquired with a novel flexible surface coil (FSC) and with a conventional endorectal coil (ERC). Methods Six radiologists independently reviewed 200 pairs of axial, high-resolution T2-weighted and diffusion-weighted image data sets, each containing one examination acquired with the FSC and one with the ERC, respectively. Readers selected their preferred examination from each pair and assessed every single examination using six quality criteria on 4-point scales. Signal-to-noise ratios were measured and compared. Results Two readers preferred FSC acquisition (36.5–45%) over ERC acquisition (13.5–15%) for both sequences combined, and four readers preferred ERC acquisition (41–46%). Analysis of pooled responses for both sequences from all readers shows no significant preference for FSC or ERC. Analysis of the individual sequences revealed a pooled preference for the FSC in T2WI (38.7% vs 17.8%) and for the ERC in DWI (50.9% vs 19.6%). Patients’ weight was the only weak predictor of a preference for the ERC acquisition (p = 0.04). SNR and CNR were significantly higher in the ERC acquisitions (pp=0.1). Conclusion Although readers have strong individual preferences, comparable subjective image quality can be obtained for prostate MRI with an ERC and the novel FSC. ERC imaging might be particularly valuable for sequences with inherently lower SNR as DWI and larger patients whereas the FSC is generally preferred in T2WI. FSC imaging generates a lower SNR than with an ERC. more...
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- 2020
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32. Effect of shelter-in-place on emergency department radiology volumes during the COVID-19 pandemic
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Marc D. Kohli, Edward J. Zaragoza, Rony Kampalath, Chantal Chahine, John Mongan, Michael Nguyentat, Justin Glavis-Bloom, Roozbeh Houshyar, Karen Tran-Harding, Paul Murphy, and Thomas W. Loehfelm
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Male ,Healthcare utilization ,Emergency Care ,California ,030218 nuclear medicine & medical imaging ,0302 clinical medicine ,Pandemic ,Viral ,Chest radiology ,Emergency Service ,Shelter in place ,Health Services ,Nuclear Medicine & Medical Imaging ,Radiology Nuclear Medicine and imaging ,Quarantine ,Emergency Medicine ,Original Article ,Female ,Radiology ,Emergency Service, Hospital ,Coronavirus Infections ,Diagnostic Imaging ,medicine.medical_specialty ,Coronavirus disease 2019 (COVID-19) ,Pneumonia, Viral ,Clinical Sciences ,Subspecialty ,Trauma ,03 medical and health sciences ,Hospital ,Betacoronavirus ,Clinical Research ,medicine ,Humans ,Radiology, Nuclear Medicine and imaging ,Pandemics ,business.industry ,SARS-CoV-2 ,Public health ,COVID-19 ,030208 emergency & critical care medicine ,Emergency department ,Pneumonia ,Coronavirus ,Good Health and Well Being ,ER ,Predictive model ,Utilization Review ,business - Abstract
Purpose The coronavirus disease 2019 (COVID-19) pandemic has led to significant disruptions in the healthcare system including surges of infected patients exceeding local capacity, closures of primary care offices, and delays of non-emergent medical care. Government-initiated measures to decrease healthcare utilization (i.e., “flattening the curve”) have included shelter-in-place mandates and social distancing, which have taken effect across most of the USA. We evaluate the immediate impact of the Public Health Messaging and shelter-in-place mandates on Emergency Department (ED) demand for radiology services. Methods We analyzed ED radiology volumes from the five University of California health systems during a 2-week time period following the shelter-in-place mandate and compared those volumes with March 2019 and early April 2019 volumes. Results ED radiology volumes declined from the 2019 baseline by 32 to 40% (p < 0.001) across the five health systems with a total decrease in volumes across all 5 systems by 35% (p < 0.001). Stratifying by subspecialty, the smallest declines were seen in non-trauma thoracic imaging, which decreased 18% (p value < 0.001), while all other non-trauma studies decreased by 48% (p < 0.001). Conclusion Total ED radiology demand may be a marker for public adherence to shelter-in-place mandates, though ED chest radiology demand may increase with an increase in COVID-19 cases. Electronic supplementary material The online version of this article (10.1007/s10140-020-01797-y) contains supplementary material, which is available to authorized users. more...
- Published
- 2020
33. Evaluating Artificial Intelligence Systems to Guide Purchasing Decisions
- Author
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John Mongan, Marc D. Kohli, and Ross W. Filice
- Subjects
Graphical processing unit ,Computer science ,business.industry ,media_common.quotation_subject ,Purchasing process ,Purchasing ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,0302 clinical medicine ,Artificial Intelligence ,030220 oncology & carcinogenesis ,Health care ,Radiologists ,Revenue ,Humans ,Radiology, Nuclear Medicine and imaging ,Quality (business) ,Artificial intelligence ,Quality of care ,business ,Implementation ,media_common ,Mammography - Abstract
Many radiologists are considering investments in artificial intelligence (AI) to improve the quality of care for our patients. This article outlines considerations for the purchasing process beginning with performance evaluation. Practices should decide whether there is a need to independently verify performance or accept vendor-provided data. Successful implementations will consider who will receive AI results, how results will be presented, and the impact on efficiency. The article provides education on infrastructure considerations including the benefits and drawbacks of best-of-breed and platform approaches in addition to highly specialized server requirements like graphical processing unit availability. Finally, the article presents financial and quality and safety considerations, some of which are unique to AI. Examples include whether additional revenue could be obtained, as in the case of mammography, and whether an AI model unintentionally leads to reinforcing healthcare disparities. more...
- Published
- 2020
34. Implementation and design of artificial intelligence in abdominal imaging
- Author
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Hailey H, Choi, Silvia D, Chang, and Marc D, Kohli
- Subjects
Diagnostic Imaging ,Machine Learning ,Male ,Artificial Intelligence ,Radiologists ,Humans ,Algorithms - Abstract
Artificial intelligence is a technique that holds promise for helping radiologists improve the care of our patients. At the same time, implementation decisions we make now can have a long-lasting effect on patient outcomes. In the following article, we discuss four areas with unique considerations for implementation of AI: bias, trust, risk, and design. In each section, we highlight applications of AI to abdominal imaging and prostate cancer specifically. more...
- Published
- 2020
35. Association between misty mesentery with baseline or new diagnosis of cancer: a matched cohort study
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Spencer C. Behr, Susana Candia, Sivan G. Marcus, John Mongan, Antonio C. Westphalen, Derek Sun, Marc D. Kohli, and Ronald J. Zagoria
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Male ,medicine.medical_specialty ,Malignancy ,Gastroenterology ,New diagnosis ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,0302 clinical medicine ,Matched cohort ,Neoplasms ,Internal medicine ,Humans ,Medicine ,Mesentery ,Radiology, Nuclear Medicine and imaging ,In patient ,Survival analysis ,Aged ,Retrospective Studies ,Genitourinary system ,business.industry ,Cancer ,Middle Aged ,medicine.disease ,medicine.anatomical_structure ,Case-Control Studies ,030220 oncology & carcinogenesis ,Female ,Tomography, X-Ray Computed ,business - Abstract
We compared the prevalence of a baseline diagnosis of cancer in patients with and without misty mesentery (MM) and determined its association with the development of a new cancer. This was a retrospective, HIPAA-compliant, IRB-approved case-control study of 148 cases and 4:1 age- and gender-matched controls. Statistical tests included chi-square, t-test, hazard models, and C-statistic. Patients with MM were less likely to have cancer at baseline (RR=0.74, p=0.003), but more likely to develop a new malignancy on follow-up (RR=2.13, p=0.003; survival analysis HR 1.74, p=0.05). MM may confer an increased probability of later developing cancer, particularly genitourinary tumors. more...
- Published
- 2018
- Full Text
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36. A Platform for Innovation and Standards Evaluation: a Case Study from the OpenMRS Open-Source Radiology Information System
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Teri S. Schmidt, Marc D. Kohli, Saptarshi Purkayastha, Judy Wawira Gichoya, and Larry Ivange
- Subjects
Diagnostic Imaging ,Imaging informatics ,Clinical Sciences ,Article ,Enterprise imaging ,Workflow ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,0302 clinical medicine ,Health care ,Humans ,Radiology Information System ,Radiology, Nuclear Medicine and imaging ,Product (category theory) ,Architecture ,Radiological and Ultrasound Technology ,business.industry ,Open source ,Computer Science Applications ,Systems Integration ,Nuclear Medicine & Medical Imaging ,Engineering management ,Radiology Information Systems ,Incentive ,Work (electrical) ,030220 oncology & carcinogenesis ,Mandate ,business ,Software - Abstract
Open-source development can provide a platform for innovation by seeking feedback from community members as well as providing tools and infrastructure to test new standards. Vendors of proprietary systems may delay adoption of new standards until there are sufficient incentives such as legal mandates or financial incentives to encourage/mandate adoption. Moreover, open-source systems in healthcare have been widely adopted in low- and middle-income countries and can be used to bridge gaps that exist in global health radiology. Since 2011, the authors, along with a community of open-source contributors, have worked on developing an open-source radiology information system (RIS) across two communities-OpenMRS and LibreHealth. The main purpose of the RIS is to implement core radiology workflows, on which others can build and test new radiology standards. This work has resulted in three major releases of the system, with current architectural changes driven by changing technology, development of new standards in health and imaging informatics, and changing user needs. At their core, both these communities are focused on building general-purpose EHR systems, but based on user contributions from the fringes, we have been able to create an innovative system that has been used by hospitals and clinics in four different countries. We provide an overview of the history of the LibreHealth RIS, the architecture of the system, overview of standards integration, describe challenges of developing an open-source product, and future directions. Our goal is to attract more participation and involvement to further develop the LibreHealth RIS into an Enterprise Imaging System that can be used in other clinical imaging including pathology and dermatology. more...
- Published
- 2018
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- View/download PDF
37. Learning HL7 FHIR Using the HAPI FHIR Server and Its Use in Medical Imaging with the SIIM Dataset
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Steve G. Langer, Marc D. Kohli, and Mohannad Hussain
- Subjects
Diagnostic Imaging ,FOSS ,SIIM Hackathon ,Computer science ,FHIR ,Interoperability ,Web APIs ,Datasets as Topic ,HAPI ,02 engineering and technology ,computer.software_genre ,Web API ,Article ,Time ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,0302 clinical medicine ,EHR (electronic health record) ,020204 information systems ,0202 electrical engineering, electronic engineering, information engineering ,Medical imaging ,Electronic Health Records ,Humans ,EPR (electronic patient record) ,Radiology, Nuclear Medicine and imaging ,Protocol (object-oriented programming) ,Health Level Seven ,Web-based technology ,Radiological and Ultrasound Technology ,Application programming interface ,Multimedia ,Health Information Interoperability ,Healthcare information technology ,Health Level 7 ,HL7 ,Fast Health Interoperability Resources ,Computer Science Applications ,Radiology Information Systems ,API ,computer ,Software ,PATH (variable) - Abstract
Health Level 7’s (HL7’s) new standard, FHIR (Fast Health Interoperability Resources), is setting healthcare information technology and medical imaging specifically ablaze with excitement. This paper aims to describe the protocol’s advantages in some detail and explore an easy path for those unfamiliar with FHIR to begin learning the standard using free, open-source tools, namely the HL7 application programming interface (HAPI) FHIR server and the SIIM Hackathon Dataset. more...
- Published
- 2018
- Full Text
- View/download PDF
38. Proving Value in Radiology: Experience Developing and Implementing a Shareable Open Source Registry Platform Driven by Radiology Workflow
- Author
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Paul Haste, Matthew S. Johnson, Elizabeth Mills Abigail, Judy Wawira Gichoya, and Marc D. Kohli
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Decision support system ,medicine.medical_specialty ,Quality management ,media_common.quotation_subject ,Clinical Sciences ,Turnaround time ,Article ,Workflow ,030218 nuclear medicine & medical imaging ,Database ,03 medical and health sciences ,0302 clinical medicine ,Clinical Research ,Overhead (business) ,Health care ,medicine ,Humans ,Radiology, Nuclear Medicine and imaging ,Quality (business) ,Registries ,media_common ,Interventional radiology ,Value based care ,Radiological and Ultrasound Technology ,business.industry ,Software as a service ,Research infrastructure ,Open source ,Health Services ,Quality Improvement ,Computer Science Applications ,Nuclear Medicine & Medical Imaging ,Good Health and Well Being ,Radiology Information Systems ,Networking and Information Technology R&D (NITRD) ,030220 oncology & carcinogenesis ,Generic health relevance ,Radiology ,business - Abstract
Numerous initiatives are in place to support value based care in radiology including decision support using appropriateness criteria, quality metrics like radiation dose monitoring, and efforts to improve the quality of the radiology report for consumption by referring providers. These initiatives are largely data driven. Organizations can choose to purchase proprietary registry systems, pay for software as a service solution, or deploy/build their own registry systems. Traditionally, registries are created for a single purpose like radiation dosage or specific disease tracking like diabetes registry. This results in a fragmented view of the patient, and increases overhead to maintain such single purpose registry system by requiring an alternative data entry workflow and additional infrastructure to host and maintain multiple registries for different clinical needs. This complexity is magnified in the health care enterprise whereby radiology systems usually are run parallel to other clinical systems due to the different clinical workflow for radiologists. In the new era of value based care where data needs are increasing with demand for a shorter turnaround time to provide data that can be used for information and decision making, there is a critical gap to develop registries that are more adapt to the radiology workflow with minimal overhead on resources for maintenance and setup. We share our experience of developing and implementing an open source registry system for quality improvement and research in our academic institution that is driven by our radiology workflow. more...
- Published
- 2017
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39. Implementing Machine Learning in Radiology Practice and Research
- Author
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Marc D. Kohli, Ross W. Filice, Luciano M. Prevedello, and J. Raymond Geis
- Subjects
medicine.medical_specialty ,Biomedical Research ,Machine learning ,computer.software_genre ,Sensitivity and Specificity ,Pattern Recognition, Automated ,030218 nuclear medicine & medical imaging ,Machine Learning ,03 medical and health sciences ,0302 clinical medicine ,Image Interpretation, Computer-Assisted ,medicine ,Humans ,Radiology, Nuclear Medicine and imaging ,Practice Patterns, Physicians' ,Class (computer programming) ,business.industry ,Reproducibility of Results ,General Medicine ,Image Enhancement ,United States ,Legal risk ,Informatics ,Radiology ,Artificial intelligence ,Hyper-heuristic ,business ,computer ,Algorithms ,030217 neurology & neurosurgery - Abstract
OBJECTIVE. The purposes of this article are to describe concepts that radiologists should understand to evaluate machine learning projects, including common algorithms, supervised as opposed to unsupervised techniques, statistical pitfalls, and data considerations for training and evaluation, and to briefly describe ethical dilemmas and legal risk. CONCLUSION. Machine learning includes a broad class of computer programs that improve with experience. The complexity of creating, training, and monitoring machine learning indicates that the success of the algorithms will require radiologist involvement for years to come, leading to engagement rather than replacement. more...
- Published
- 2017
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- View/download PDF
40. Assessment of an ensemble of machine learning models toward abnormality detection in chest radiographs
- Author
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Marc D. Kohli, Sudhir Sornapudi, Sameer Antani, and Sivaramakrishnan Rajaraman
- Subjects
Computer science ,Feature extraction ,Respiratory Tract Diseases ,02 engineering and technology ,Overfitting ,Machine learning ,computer.software_genre ,Convolutional neural network ,030218 nuclear medicine & medical imaging ,Machine Learning ,03 medical and health sciences ,0302 clinical medicine ,Robustness (computer science) ,0202 electrical engineering, electronic engineering, information engineering ,Image Processing, Computer-Assisted ,Humans ,Training set ,business.industry ,Deep learning ,X-Rays ,Ensemble learning ,Task analysis ,020201 artificial intelligence & image processing ,Radiography, Thoracic ,Artificial intelligence ,Neural Networks, Computer ,business ,computer ,Algorithms - Abstract
Respiratory diseases account for a significant proportion of deaths and disabilities across the world. Chest X-ray (CXR) analysis remains a common diagnostic imaging modality for confirming intra-thoracic cardiopulmonary abnormalities. However, there remains an acute shortage of expert radiologists, particularly in under-resourced settings, resulting in severe interpretation delays. These issues can be mitigated by a computer-aided diagnostic (CADx) system to supplement decision-making and improve throughput while preserving and possibly improving the standard-of-care. Systems reported in the literature or popular media use handcrafted features and/or data-driven algorithms like deep learning (DL) to learn underlying data distributions. The remarkable success of convolutional neural networks (CNN) toward image recognition tasks has made them a promising choice for automated medical image analyses. However, CNNs suffer from high variance and may overfit due to their sensitivity to training data fluctuations. Ensemble learning helps to reduce this variance by combining predictions of multiple learning algorithms to construct complex, non-linear functions and improve robustness and generalization. This study aims to construct and assess the performance of an ensemble of machine learning (ML) models applied to the challenge of classifying normal and abnormal CXRs and significantly reducing the diagnostic load of radiologists and primary-care physicians. more...
- Published
- 2020
41. Collaborative Opportunities for Radiology Quality Improvement Projects
- Author
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Marc D. Kohli, Robert M. Hicks, K. Pallav Kolli, Karen G. Ordovas, David Seidenwurm, Kesav Raghavan, and Jason N. Itri
- Subjects
medicine.medical_specialty ,Quality management ,business.industry ,medicine ,MEDLINE ,Radiology, Nuclear Medicine and imaging ,Medical physics ,business - Published
- 2020
42. Ethics, Artificial Intelligence, and Radiology
- Author
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Marc D. Kohli and Raym Geis
- Subjects
Information retrieval ,MEDLINE ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,0302 clinical medicine ,Artificial Intelligence ,Codes of Ethics ,030220 oncology & carcinogenesis ,Humans ,Radiology, Nuclear Medicine and imaging ,Radiology ,Psychology ,Algorithms ,Ethical code - Published
- 2018
- Full Text
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43. Ethics of artificial intelligence in radiology:summary of the joint European and North American multisociety statement
- Author
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Marc D. Kohli, Robert van den Hoven van Genderen, Nabile M. Safdar, Elmar Kotter, J. Raymond Geis, Jack Spencer, Jacob L. Jaremko, Erik Ranschaert, Andrea Borondy Kitts, Tessa S. Cook, William F. Shields, Matthew B. Morgan, Judy Birch, Carol C. Wu, Steve G. Langer, An Tang, Judy Wawira Gichoya, Adrian P. Brady, Internet Law, Kooijmans Institute, Network Institute, and Boundaries of Law more...
- Subjects
Imaging informatics ,Statement (logic) ,030218 nuclear medicine & medical imaging ,Machine Learning ,0302 clinical medicine ,Medicine ,Societies, Medical ,media_common ,Data ,General Medicine ,Justice and Strong Institutions ,Ethics of artificial intelligence ,Europe ,030220 oncology & carcinogenesis ,Accountability ,Practice Guidelines as Topic ,symbols ,Radiology ,Psychology ,lcsh:Medical physics. Medical radiology. Nuclear medicine ,Canada ,medicine.medical_specialty ,Consensus ,SDG 16 - Peace ,lcsh:R895-920 ,media_common.quotation_subject ,education ,symbols.namesake ,03 medical and health sciences ,Dignity ,Artificial Intelligence ,Codes of Ethics ,Radiologists ,Machine learning ,Humans ,Radiology, Nuclear Medicine and imaging ,Ethical code ,Ethics ,business.industry ,SDG 16 - Peace, Justice and Strong Institutions ,Shapley value ,Transparency (behavior) ,United States ,Harm ,Nash equilibrium ,Informatics ,North America ,Joint (building) ,Statement ,business - Abstract
This is a condensed summary of an international multisociety statement on ethics of artificial intelligence (AI) in radiology produced by the ACR, European Society of Radiology, RSNA, Society for Imaging Informatics in Medicine, European Society of Medical Imaging Informatics, Canadian Association of Radiologists, and American Association of Physicists in Medicine. AI has great potential to increase efficiency and accuracy throughout radiology, but also carries inherent pitfalls and biases. Widespread use of AI-based intelligent and autonomous systems in radiology can increase the risk of systemic errors with high consequence, and highlights complex ethical and societal issues. Currently, there is little experience using AI for patient care in diverse clinical settings. Extensive research is needed to understand how to best deploy AI in clinical practice. This statement highlights our consensus that ethical use of AI in radiology should promote well-being, minimize harm, and ensure that the benefits and harms are distributed among stakeholders in a just manner. We believe AI should respect human rights and freedoms, including dignity and privacy. It should be designed for maximum transparency and dependability. Ultimate responsibility and accountability for AI remains with its human designers and operators for the foreseeable future. The radiology community should start now to develop codes of ethics and practice for AI which promote any use that helps patients and the common good and should block use of radiology data and algorithms for financial gain without those two attributes. Electronic supplementary material The online version of this article (10.1186/s13244-019-0785-8) contains supplementary material, which is available to authorized users. more...
- Published
- 2019
- Full Text
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44. Describing Disease-specific Reporting Guidelines: A Brief Guide for Radiologists
- Author
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Krishna Juluru, Marc D. Kohli, and Marta E. Heilbrun
- Subjects
Disease specific ,medicine.medical_specialty ,Quality Assurance, Health Care ,business.industry ,MEDLINE ,Guidelines as Topic ,Radiology Information Systems ,Health care ,Medicine ,Humans ,Radiology, Nuclear Medicine and imaging ,Medical physics ,Interdisciplinary communication ,Interdisciplinary Communication ,Periodicals as Topic ,business ,Quality assurance - Published
- 2019
45. A Practice Quality Improvement Project: Reducing Dose of Routine Chest CT Imaging in a Busy Clinical Practice
- Author
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Marc D. Kohli, Edwin A. Takahashi, and Shawn D. Teague
- Subjects
medicine.medical_specialty ,Certification ,Quality management ,Image quality ,Thoracic ,Clinical Sciences ,Chest ct ,Dose reduction ,Iterative reconstruction ,Radiation Dosage ,Effective dose (radiation) ,Article ,030218 nuclear medicine & medical imaging ,7.3 Management and decision making ,Maintenance of Certification ,03 medical and health sciences ,chemistry.chemical_compound ,0302 clinical medicine ,Clinical Research ,Multidetector Computed Tomography ,Humans ,Medicine ,Idose ,Radiology, Nuclear Medicine and imaging ,Medical physics ,Computed tomography ,Radiological and Ultrasound Technology ,business.industry ,Radiation Exposure ,Quality Improvement ,Computer Science Applications ,Radiography ,Clinical Practice ,Nuclear Medicine & Medical Imaging ,chemistry ,030220 oncology & carcinogenesis ,Biomedical Imaging ,Radiography, Thoracic ,Management of diseases and conditions ,Radiology ,business ,Algorithms - Abstract
The purpose of this report is to describe our experience with the implementation of a practice quality improvement (PQI) project in thoracic imaging as part of the American Board of Radiology Maintenance of Certification process. The goal of this PQI project was to reduce the effective radiation dose of routine chest CT imaging in a busy clinical practice by employing the iDose(4) (Philips Healthcare) iterative reconstruction technique. The dose reduction strategy was implemented in a stepwise process on a single 64-slice CT scanner with a volume of 1141 chest CT scans during the year. In the first annual quarter, a baseline effective dose was established using the standard filtered back projection (FBP) algorithm protocol and standard parameters such as kVp and mAs. The iDose(4) technique was then applied in the second and third annual quarters while keeping all other parameters unchanged. In the fourth quarter, a reduction in kVp was also implemented. Throughout the process, the images were continually evaluated to assure that the image quality was comparable to the standard protocol from multiple other scanners. Utilizing a stepwise approach, the effective radiation dose was reduced by 23.62 and 43.63% in quarters two and four, respectively, compared to our initial standard protocol with no perceived difference in diagnostic quality. This practice quality improvement project demonstrated a significant reduction in the effective radiation dose of thoracic CT scans in a busy clinical practice. more...
- Published
- 2016
- Full Text
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46. Augmenting the National Institutes of Health Chest Radiograph Dataset with Expert Annotations of Possible Pneumonia
- Author
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Ritu R. Gill, Myrna C. B. Godoy, Safwan Halabi, Tessa S. Cook, Veronica Arteaga, Marc D. Kohli, Dharshan Vummidi, Kavitha Yaddanapudi, George Shih, Archana T Laroia, Stephen B. Hobbs, Carol C. Wu, Maya Galperin-Aizenberg, Arjun Sharma, Jean Jeudy, Palmi Shah, Judith K. Amorosa, Anouk Stein, and Luciano M. Prevedello more...
- Subjects
medicine.medical_specialty ,Radiological and Ultrasound Technology ,medicine.diagnostic_test ,business.industry ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Pneumonia ,medicine.disease ,Data Resources ,respiratory tract diseases ,Infectious Diseases ,Artificial Intelligence ,Pneumonia & Influenza ,medicine ,Radiology, Nuclear Medicine and imaging ,Medical physics ,Patient Safety ,Chest radiograph ,business ,Lung - Abstract
This dataset is intended to be used for machine learning and is composed of annotations with bounding boxes for pulmonary opacity on chest radiographs which may represent pneumonia in the appropriate clinical setting. more...
- Published
- 2019
47. Artificial Intelligence and Human Life: Five Lessons for Radiology from the 737 MAX Disasters
- Author
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Marc D. Kohli and John Mongan
- Subjects
Editorial ,Radiological and Ultrasound Technology ,Artificial Intelligence ,Computer science ,Human life ,Radiology, Nuclear Medicine and imaging ,Data science - Published
- 2020
- Full Text
- View/download PDF
48. Machine Learning in Neurooncology Imaging: From Study Request to Diagnosis and Treatment
- Author
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Javier Villanueva-Meyer, Janine M. Lupo, Adam E. Flanders, Marc D. Kohli, Christopher P. Hess, and Peter Chang
- Subjects
business.industry ,Brain Neoplasms ,Neurooncology ,Neuroimaging ,General Medicine ,Machine learning ,computer.software_genre ,030218 nuclear medicine & medical imaging ,Variety (cybernetics) ,Machine Learning ,03 medical and health sciences ,0302 clinical medicine ,030220 oncology & carcinogenesis ,Key (cryptography) ,Medical imaging ,Medicine ,Humans ,Radiology, Nuclear Medicine and imaging ,Artificial intelligence ,business ,computer ,Algorithms - Abstract
Machine learning has potential to play a key role across a variety of medical imaging applications. This review seeks to elucidate the ways in which machine learning can aid and enhance diagnosis, treatment, and follow-up in neurooncology.Given the rapid pace of development in machine learning over the past several years, a basic proficiency of the key tenets and use cases in the field is critical to assessing potential opportunities and challenges of this exciting new technology. more...
- Published
- 2018
49. Bending the Artificial Intelligence Curve for Radiology: Informatics Tools From ACR and RSNA
- Author
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Adam E. Flanders, Keith J. Dreyer, Charles E. Kahn, Ken Wang, Marc D. Kohli, Marta E. Heilbrun, and Tarik K. Alkasab
- Subjects
medicine.medical_specialty ,Decision support system ,Computer science ,Interoperability ,GeneralLiterature_MISCELLANEOUS ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,0302 clinical medicine ,Health informatics tools ,Artificial Intelligence ,medicine ,Humans ,Radiology, Nuclear Medicine and imaging ,Use case ,Medical Informatics Applications ,Registries ,Societies, Medical ,LOINC ,business.industry ,Computer assistance ,Radiology report ,030220 oncology & carcinogenesis ,Informatics ,Practice Guidelines as Topic ,Radiology ,Artificial intelligence ,Diffusion of Innovation ,business - Abstract
Artificial intelligence (AI) will reshape radiology over the coming years. The radiology community has a strong history of embracing new technology for positive change, and AI is no exception. As with any new technology, rapid, successful implementation faces several challenges that will require creation and adoption of new integration technology. Use cases important to real-world application of AI are described, including clinical registries, AI research, AI product validation, and computer assistance for radiology reporting. Furthermore, the informatics technologies required for successful implementation of the use cases are described, including open Computer-Assisted Radiologist Decision Support, ACR Assist, ACR Data Science Institute use cases, common data elements (radelement.org), RadLex (radlex.org), LOINC/RSNA RadLex Playbook (loinc.org), and Radiology Report Templates (radreport.org). more...
- Published
- 2018
50. Social Media Tools for Department and Practice Communication and Branding in the Digital Age
- Author
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Dania Daye, Marc D. Kohli, Amy L. Kotsenas, Marta E. Heilbrun, and Alexander J. Towbin
- Subjects
Practice Management ,Radiology Department ,Process (engineering) ,Clinical Sciences ,Target audience ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,Hospital ,0302 clinical medicine ,Advertising ,Medical ,Practice Management, Medical ,Medicine ,Humans ,Radiology, Nuclear Medicine and imaging ,Social media ,030212 general & internal medicine ,Set (psychology) ,Strategic planning ,Radiology Department, Hospital ,business.industry ,Advertising as Topic ,Planning Techniques ,Public relations ,United States ,Identification (information) ,Nuclear Medicine & Medical Imaging ,Resource allocation ,Tracking (education) ,business ,Social Media - Abstract
With nearly 70% of adults in the United States using at least one social media platform, a social media presence is increasingly important for departments and practices. Patients, prospective faculty and trainees, and referring physicians look to social media to find information about our organizations. The authors present a stepwise process for planning, executing, and evaluating an organizational social media strategy. This process begins with alignment with a strategic plan to set goals, identification of the target audience(s), selection of appropriate social media channels, tracking effectiveness, and resource allocation. The article concludes with a discussion of advantages and disadvantages of social media through a review of current literature. ©RSNA, 2018. more...
- Published
- 2018
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