Artificial Intelligence and Positron Emission Tomography Imaging Workflow:: Technologists' Perspective

Impact of intelligent virtual and AI-based automated collimation functionalities on the efficiency of radiographic acquisitions

INTRODUCTION

Intelligent virtual and AI-based collimation functionalities have the potential to enable an efficient workflow for radiographers, but the specific impact on clinical routines is still unknown. This study analyzes primarily the influence of intelligent collimation functionalities on the examination time and the number of needed interactions with the radiography system.

METHODS

An observational study was conducted on the use of three camera-based intelligent features at five clinical sites in Europe and the USA: AI-based auto thorax collimation (ATC), smart virtual ortho (SVO) collimation for stitched long-leg and full-spine examinations, and virtual collimation (VC) at the radiography system workstation. Two people conducted semi-structured observations during routine examinations to collect data with the functionalities either activated or deactivated.

RESULTS

Median exam duration was 31 vs. 45 s (p < 0.0001) for 95 thorax examinations with ATC and 94 without ATC. For stitched orthopedic examinations, 34 were performed with SVO and 40 without SVO, and the median exam duration was 62 vs. 82 s (p < 0.0001). The median time for setting the ortho range - i.e., the time between setting the upper and the lower limits of the collimation field - was 7 vs. 16 s for 39 examinations with SVO and 43 without SVO (p < 0.0001). In 109 thorax examinations with ATC and 112 without ATC, the median number of system interactions was 1 vs. 2 (p < 0.0001). VC was used to collimate in 2.4% and to check the collimation field in 68.5% of 292 observed chest and other examinations.

CONCLUSION

ATC and SVO enable the radiographer to save time during chest or stitched examinations. Additionally, ATC reduces machine interactions during chest examinations.

IMPLICATIONS FOR PRACTICE

System and artificial intelligence can support the radiographer during the image acquisition by providing a more efficient workflow.

A Systematic Review on Artificial Intelligence Evaluating Metastatic Prostatic Cancer and Lymph Nodes on PSMA PET Scans

Early detection of metastatic prostate cancer (mPCa) is crucial. Whilst the prostate-specific membrane antigen (PSMA) PET scan has high diagnostic accuracy, it suffers from inter-reader variability, and the time-consuming reporting process. This systematic review was registered on PROSPERO (ID CRD42023456044) and aims to evaluate AI's ability to enhance reporting, diagnostics, and predictive capabilities for mPCa on PSMA PET scans. Inclusion criteria covered studies using AI to evaluate mPCa on PSMA PET, excluding non-PSMA tracers. A search was conducted on Medline, Embase, and Scopus from inception to July 2023. After screening 249 studies, 11 remained eligible for inclusion. Due to the heterogeneity of studies, meta-analysis was precluded. The prediction model risk of bias assessment tool (PROBAST) indicated a low overall risk of bias in ten studies, though only one incorporated clinical parameters (such as age, and Gleason score). AI demonstrated a high accuracy (98%) in identifying lymph node involvement and metastatic disease, albeit with sensitivity variation (62-97%). Advantages included distinguishing bone lesions, estimating tumour burden, predicting treatment response, and automating tasks accurately. In conclusion, AI showcases promising capabilities in enhancing the diagnostic potential of PSMA PET scans for mPCa, addressing current limitations in efficiency and variability.

Role of Artificial Intelligence in PET/CT Imaging for Management of Lymphoma

Our review shows that AI-based analysis of lymphoma whole-body FDG-PET/CT can inform all phases of clinical management including staging, prognostication, treatment planning, and treatment response evaluation. We highlight advancements in the role of neural networks for performing automated image segmentation to calculate PET-based imaging biomarkers such as the total metabolic tumor volume (TMTV). AI-based image segmentation methods are at levels where they can be semi-automatically implemented with minimal human inputs and nearing the level of a second-opinion radiologist. Advances in automated segmentation methods are particularly apparent in the discrimination of lymphomatous vs non-lymphomatous FDG-avid regions, which carries through to automated staging. Automated TMTV calculators, in addition to automated calculation of measures such as Dmax are informing robust models of progression-free survival which can then feed into improved treatment planning.

Artificial Intelligence in Nuclear Medicine: Opportunities, Challenges, and Responsibilities Toward a Trustworthy Ecosystem

Trustworthiness is a core tenet of medicine. The patient-physician relationship is evolving from a dyad to a broader ecosystem of health care. With the emergence of artificial intelligence (AI) in medicine, the elements of trust must be revisited. We envision a road map for the establishment of trustworthy AI ecosystems in nuclear medicine. In this report, AI is contextualized in the history of technologic revolutions. Opportunities for AI applications in nuclear medicine related to diagnosis, therapy, and workflow efficiency, as well as emerging challenges and critical responsibilities, are discussed. Establishing and maintaining leadership in AI require a concerted effort to promote the rational and safe deployment of this innovative technology by engaging patients, nuclear medicine physicians, scientists, technologists, and referring providers, among other stakeholders, while protecting our patients and society. This strategic plan was prepared by the AI task force of the Society of Nuclear Medicine and Molecular Imaging.

An application of quality function deployment and SERVQUAL approaches to enhance the service quality in radiology centres

Purpose

Meeting the patients' requirements as customers of the health care sector is crucially important as a social responsibility. According to the resource constraints, only an efficient utilisation of health services can provide that purpose. This study aims to develop a quantitative assessment framework for radiology centres as a vital section in healthcare to translate the patients' requirements into service quality specifications. This would help to achieve quality improvement by emphasising the voice of customers.

Design/methodology/approach

A literature review is conducted to specify the service quality criteria and the patients' requirements related to healthcare and hospitals. Based on the experts' opinions, these criteria and requirements are later customised for the radiology centres. Moreover, the requirements are categorised into five dimensions of SERVQUAL. The interrelations between service elements are also determined through expert group consensus using Pearson correlation. Afterwards, by applying the QFD method, the relations between the requirements and criteria are explored. Additionally, a customer satisfaction survey is executed in Tehran public hospitals to prioritise these requirements and provide an importance-satisfaction analysis.

Findings

Based on the result of the case study, service elements are prioritised for improvement, and practical suggestions are provided using the Delphi technique for quality improvement. In addition, a cause-and-effect diagram is presented to highlight the improvement area and provide enhancement suggestions.

Originality/value

This study is the first empirical attempt to benefit from the VOC in evaluating and enhancing the quality of service delivered to radiology patients. In doing so, the study applies a hybrid approach of QFD and SERVQUAL as well as other tools to highlight the improvement area and provide enhancement suggestions. The findings can be readily used by the practitioners.