Automated Billing Code Retrieval from MRI Scanner Log Data

Natural language processing in radiology: Clinical applications and future directions

Natural language processing (NLP) is a wide range of techniques that allows computers to interact with human text. Applications of NLP in everyday life include language translation aids, chat bots, and text prediction. It has been increasingly utilized in the medical field with increased reliance on electronic health records. As findings in radiology are primarily communicated via text, the field is particularly suited to benefit from NLP based applications. Furthermore, rapidly increasing imaging volume will continue to increase burden on clinicians, emphasizing the need for improvements in workflow. In this article, we highlight the numerous non-clinical, provider focused, and patient focused applications of NLP in radiology. We also comment on challenges associated with development and incorporation of NLP based applications in radiology as well as potential future directions.

Automated Protocoling for MRI Exams-Challenges and Solutions

Automated protocoling for MRI examinations is an amendable target for workflow automation with artificial intelligence. However, there are still challenges to overcome for a successful and robust approach. These challenges are outlined and analyzed in this work. Through a literature review, we analyzed limitations of currently published approaches for automated protocoling. Then, we assessed these limitations quantitatively based on data from a private radiology practice. For this, we assessed the information content provided by the clinical indication by computing the overlap coefficients for the sets of ICD-10-coded admitting diagnoses of different MRI protocols. Additionally, we assessed the heterogeneity of protocol trees from three different MRI scanners based on the overlap coefficient, on MRI protocol and sequence level. Additionally, we applied sequence name standardization to demonstrate its effect on the heterogeneity assessment, i.e., the overlap coefficient, of different protocol trees. The overlap coefficient for the set of ICD-10-coded admitting diagnoses for different protocols ranges from 0.14 to 0.56 for brain/head MRI exams and 0.04 to 0.57 for spine exams. The overlap coefficient across the set of sequences used at two different scanners increases when applying sequence name standardization (from 0.81/0.86 to 0.93). Automated protocoling for MRI examinations has the potential to reduce the workload for radiologists. However, an automated protocoling approach cannot be solely based on admitting diagnosis as it does not provide sufficient information. Moreover, sequence name standardization increases the overlap coefficient across the set of sequences used at different scanners and therefore facilitates transfer learning.

Overview of Noninterpretive Artificial Intelligence Models for Safety, Quality, Workflow, and Education Applications in Radiology Practice

Artificial intelligence has become a ubiquitous term in radiology over the past several years, and much attention has been given to applications that aid radiologists in the detection of abnormalities and diagnosis of diseases. However, there are many potential applications related to radiologic image quality, safety, and workflow improvements that present equal, if not greater, value propositions to radiology practices, insurance companies, and hospital systems. This review focuses on six major categories for artificial intelligence applications: study selection and protocoling, image acquisition, worklist prioritization, study reporting, business applications, and resident education. All of these categories can substantially affect different aspects of radiology practices and workflows. Each of these categories has different value propositions in terms of whether they could be used to increase efficiency, improve patient safety, increase revenue, or save costs. Each application is covered in depth in the context of both current and future areas of work. Keywords: Use of AI in Education, Application Domain, Supervised Learning, Safety © RSNA, 2022.

Noninterpretive Uses of Artificial Intelligence in Radiology

We deem a computer to exhibit artificial intelligence (AI) when it performs a task that would normally require intelligent action by a human. Much of the recent excitement about AI in the medical literature has revolved around the ability of AI models to recognize anatomy and detect pathology on medical images, sometimes at the level of expert physicians. However, AI can also be used to solve a wide range of noninterpretive problems that are relevant to radiologists and their patients. This review summarizes some of the newer noninterpretive uses of AI in radiology.

Interactive, Up-to-date Meta-Analysis of MRI in the Management of Men with Suspected Prostate Cancer

The aim of this study was to test an interactive up-to-date meta-analysis (iu-ma) of studies on MRI in the management of men with suspected prostate cancer. Based on the findings of recently published systematic reviews and meta-analyses, two freely accessible dynamic meta-analyses (https://iu-ma.org) were designed using the programming language R in combination with the package "shiny." The first iu-ma compares the performance of the MRI-stratified pathway and the systematic transrectal ultrasound-guided biopsy pathway for the detection of clinically significant prostate cancer, while the second iu-ma focuses on the use of biparametric versus multiparametric MRI for the diagnosis of prostate cancer. Our iu-mas allow for the effortless addition of new studies and data, thereby enabling physicians to keep track of the most recent scientific developments without having to resort to classical static meta-analyses that may become outdated in a short period of time. Furthermore, the iu-mas enable in-depth subgroup analyses by a wide variety of selectable parameters. Such an analysis is not only tailored to the needs of the reader but is also far more comprehensive than a classical meta-analysis. In that respect, following multiple subgroup analyses, we found that even for various subgroups, detection rates of prostate cancer are not different between biparametric and multiparametric MRI. Secondly, we could confirm the favorable influence of MRI biopsy stratification for multiple clinical scenarios. For the future, we envisage the use of this technology in addressing further clinical questions of other organ systems.

MR-contrast-aware image-to-image translations with generative adversarial networks

Purpose

A magnetic resonance imaging (MRI) exam typically consists of several sequences that yield different image contrasts. Each sequence is parameterized through multiple acquisition parameters that influence image contrast, signal-to-noise ratio, acquisition time, and/or resolution. Depending on the clinical indication, different contrasts are required by the radiologist to make a diagnosis. As MR sequence acquisition is time consuming and acquired images may be corrupted due to motion, a method to synthesize MR images with adjustable contrast properties is required.

Methods

Therefore, we trained an image-to-image generative adversarial network conditioned on the MR acquisition parameters repetition time and echo time. Our approach is motivated by style transfer networks, whereas the “style” for an image is explicitly given in our case, as it is determined by the MR acquisition parameters our network is conditioned on.

Results

This enables us to synthesize MR images with adjustable image contrast. We evaluated our approach on the fastMRI dataset, a large set of publicly available MR knee images, and show that our method outperforms a benchmark pix2pix approach in the translation of non-fat-saturated MR images to fat-saturated images. Our approach yields a peak signal-to-noise ratio and structural similarity of 24.48 and 0.66, surpassing the pix2pix benchmark model significantly.

Conclusion

Our model is the first that enables fine-tuned contrast synthesis, which can be used to synthesize missing MR-contrasts or as a data augmentation technique for AI training in MRI. It can also be used as basis for other image-to-image translation tasks within medical imaging, e.g., to enhance intermodality translation (MRI → CT) or 7 T image synthesis from 3 T MR images.

Neural Machine Translation-Based Automated Current Procedural Terminology Classification System Using Procedure Text: Development and Validation Study

BACKGROUND

Administrative costs for billing and insurance-related activities in the United States are substantial. One critical cause of the high overhead of administrative costs is medical billing errors. With advanced deep learning techniques, developing advanced models to predict hospital and professional billing codes has become feasible. These models can be used for administrative cost reduction and billing process improvements.

OBJECTIVE

In this study, we aim to develop an automated anesthesiology current procedural terminology (CPT) prediction system that translates manually entered surgical procedure text into standard forms using neural machine translation (NMT) techniques. The standard forms are calculated using similarity scores to predict the most appropriate CPT codes. Although this system aims to enhance medical billing coding accuracy to reduce administrative costs, we compare its performance with that of previously developed machine learning algorithms.

METHODS

We collected and analyzed all operative procedures performed at Michigan Medicine between January 2017 and June 2019 (2.5 years). The first 2 years of data were used to train and validate the existing models and compare the results from the NMT-based model. Data from 2019 (6-month follow-up period) were then used to measure the accuracy of the CPT code prediction. Three experimental settings were designed with different data types to evaluate the models. Experiment 1 used the surgical procedure text entered manually in the electronic health record. Experiment 2 used preprocessing of the procedure text. Experiment 3 used preprocessing of the combined procedure text and preoperative diagnoses. The NMT-based model was compared with the support vector machine (SVM) and long short-term memory (LSTM) models.

RESULTS

The NMT model yielded the highest top-1 accuracy in experiments 1 and 2 at 81.64% and 81.71% compared with the SVM model (81.19% and 81.27%, respectively) and the LSTM model (80.96% and 81.07%, respectively). The SVM model yielded the highest top-1 accuracy of 84.30% in experiment 3, followed by the LSTM model (83.70%) and the NMT model (82.80%). In experiment 3, the addition of preoperative diagnoses showed 3.7%, 3.2%, and 1.3% increases in the SVM, LSTM, and NMT models in top-1 accuracy over those in experiment 2, respectively. For top-3 accuracy, the SVM, LSTM, and NMT models achieved 95.64%, 95.72%, and 95.60% for experiment 1, 95.75%, 95.67%, and 95.69% for experiment 2, and 95.88%, 95.93%, and 95.06% for experiment 3, respectively.

CONCLUSIONS

This study demonstrates the feasibility of creating an automated anesthesiology CPT classification system based on NMT techniques using surgical procedure text and preoperative diagnosis. Our results show that the performance of the NMT-based CPT prediction system is equivalent to that of the SVM and LSTM prediction models. Importantly, we found that including preoperative diagnoses improved the accuracy of using the procedure text alone.

Automatic, log file-based process analysis of a clinical 1.5T MR scanner: a proof-of-concept study

PURPOSE

 In light of the steadily increasing need for economical efficacy and capacity utilization it was the aim of this proof-of-concept work to implement an automated logfile-based analysis tool for MRI scanner utilization and to establish a process analysis. As a primary step, analyses of scanner and protocol utilization, parametrization of protocol processes, their durations, age dependency, and scan efficacy were to be tested.

MATERIALS AND METHODS

 Logfiles were continuously extracted from a 1.5 T MR scanner (Philips Achieva) and automatically explored for relevant scan parameters. Parameters were extracted into a database and logically combined to protocol parameters. Visualization was achieved using PowerBI (Microsoft, USA). Data aggregation comprised a day-based and protocol-based strategy. In addition, age- and regional-based testing was performed. The frequency of protocol usage was evaluated and those protocols with frequent usage compared regarding efficacy to those rarely used.

RESULTS

 After successful technical implementation, 3659 MR exams were available for further analysis. Out of a plethora of parameters, those relevant to the understanding of the scan process were identified. The initial results mirror the daily scanner usage and allow identifying, e. g., shortened scanner usage on Fridays or longer examination times in children. A scan efficacy of 69.6 ± 17.6 % excluding preparation process was identified as a parameter with high potential to be optimized in daily routine.

CONCLUSION

 The logfile-based analysis of MR scanner processes was successfully introduced and holds the promise to be extended into a comprehensive analytic tool for the analysis and optimization of scanner processes. In combination with other variables from the departmental or institutional infrastructure or patient-specific information such tool may be developed into a intelligent steering tool.

KEY POINTS

  · The automated log file analysis of MR-scanner processes was successfully introduced. · The log file-analysis allows for a detailed analysis of scanner processes. · From a log file-analysis, there is potential benefit to users, applications specialists and developers.

CITATION FORMAT

· Frydrychowicz A, Boppel T, Sieber V et al. Automatic, log file-based process analysis of a clinical 1.5T MR scanner: a proof-of-concept study. Fortschr Röntgenstr 2021; 193: 919 - 927.