Strategies for Implementing Machine Learning Algorithms in the Clinical Practice of Radiology

Abstract

Despite recent advancements in machine learning (ML) applications in health care, there have been few benefits and improvements to clinical medicine in the hospital setting. To facilitate clinical adaptation of methods in ML, this review proposes a standardized framework for the step-by-step implementation of artificial intelligence into the clinical practice of radiology that focuses on three key components: problem identification, stakeholder alignment, and pipeline integration. A review of the recent literature and empirical evidence in radiologic imaging applications justifies this approach and offers a discussion on structuring implementation efforts to help other hospital practices leverage ML to improve patient care. Clinical trial registration no. 04242667 © RSNA, 2024 Supplemental material is available for this article.

Graphical abstract

Figure 1:

Overview of Penn Medicine BioBank (PMBB) imaging data. (A) Bar graph shows the number of studies within the Penn Medicine BioBank by imaging modality. The number of studies per Penn Medicine BioBank capita is the average number of studies per patient within the Penn Medicine BioBank. (B) Line graph shows the number of imaging studies acquired per year contained within the Penn Medicine BioBank by imaging modality. (C) Line graph of 1–CDF, where CDF is the cumulative distribution function. 1–CDF corresponds to the proportion of patients (by modality) according to number of examinations. (D) Histogram shows the time between sequential repeat imaging studies by patient for the four most common imaging modalities.

Figure 2:

Comparisons of principal component distributions of six artificial intelligence–extracted image-derived phenotype (IDP) metrics (36), calculated from abdominal CT in 1276 anonymized patients from the Penn Medicine BioBank. These image-derived phenotypes included liver CT attenuation, spleen CT attenuation, liver volume, spleen volume, visceral fat volume, and subcutaneous fat volume. Using principal component analysis (PCA), the principal component of these image-derived phenotypes was extracted and its distribution was plotted as a histogram for patients stratified by different clinical diagnoses. Bar graphs show different image-derived phenotype principal component distributions in patients without diagnoses (gray bars) versus in patients diagnosed with (A) obesity (n = 91), (B) obstructive sleep apnea (n = 201), and (C) hypertension (n = 1082). Image-derived phenotype principal component distributions in patients without diagnoses (gray bars) versus in patients diagnosed with (D) nonalcoholic fatty liver disease (NAFLD; n = 429) and (E) diabetes (n = 790). (F) Genitourinary diseases (n = 1202), which are not clinically associated with these image-derived phenotype metrics, were not associated with a statistically significant different principal component distribution compared with healthy patients (P = .12). P values were calculated with statistical analyses comparing distributions of patients with and without disease diagnoses, with a two-sample Kolmogorov-Smirnov test for goodness of fit.

Figure 3:

Overview of common frameworks for integrating machine learning and medical imaging. (A) Artificial intelligence (AI) models interface directly with raw acquired data and are implemented on a per-scanner basis. (B) Machine learning models have access to the processed image outputs from the individual scanners before they are sent to the central picture archiving and communication system (PACS) server. (C) Models communicate with the picture archiving and communication system server to obtain relevant inputs for AI interpretation.

Figure 4:

Research directions for future work (93,99).