Multi-vendor evaluation of artificial intelligence as an independent reader for double reading in breast cancer screening on 275,900 mammograms.

BACKGROUND: Double reading (DR) in screening mammography increases cancer detection and lowers recall rates, but has sustainability challenges due to workforce shortages. Artificial intelligence (AI) as an independent reader (IR) in DR may provide a cost-effective solution with the potential to improve screening performance. Evidence for AI to generalise across different patient populations, screening programmes and equipment vendors, however, is still lacking. METHODS: This retrospective study simulated DR with AI as an IR, using data representative of real-world deployments (275,900 cases, 177,882 participants) from four mammography equipment vendors, seven screening sites, and two countries. Non-inferiority and superiority were assessed for relevant screening metrics. RESULTS: DR with AI, compared with human DR, showed at least non-inferior recall rate, cancer detection rate, sensitivity, specificity and positive predictive value (PPV) for each mammography vendor and site, and superior recall rate, specificity, and PPV for some. The simulation indicates that using AI would have increased arbitration rate (3.3% to 12.3%), but could have reduced human workload by 30.0% to 44.8%. CONCLUSIONS: AI has potential as an IR in the DR workflow across different screening programmes, mammography equipment and geographies, substantially reducing human reader workload while maintaining or improving standard of care. TRIAL REGISTRATION: ISRCTN18056078 (20/03/2019; retrospectively registered).

Prospective implementation of AI-assisted screen reading to improve early detection of breast cancer.

Artificial intelligence (AI) has the potential to improve breast cancer screening; however, prospective evidence of the safe implementation of AI into real clinical practice is limited. A commercially available AI system was implemented as an additional reader to standard double reading to flag cases for further arbitration review among screened women. Performance was assessed prospectively in three phases: a single-center pilot rollout, a wider multicenter pilot rollout and a full live rollout. The results showed that, compared to double reading, implementing the AI-assisted additional-reader process could achieve 0.7-1.6 additional cancer detection per 1,000 cases, with 0.16-0.30% additional recalls, 0-0.23% unnecessary recalls and a 0.1-1.9% increase in positive predictive value (PPV) after 7-11% additional human reads of AI-flagged cases (equating to 4-6% additional overall reading workload). The majority of cancerous cases detected by the AI-assisted additional-reader process were invasive (83.3%) and small-sized (≤10 mm, 47.0%). This evaluation suggests that using AI as an additional reader can improve the early detection of breast cancer with relevant prognostic features, with minimal to no unnecessary recalls. Although the AI-assisted additional-reader workflow requires additional reads, the higher PPV suggests that it can increase screening effectiveness.

Artificial Intelligence as Supporting Reader in Breast Screening: A Novel Workflow to Preserve Quality and Reduce Workload.

OBJECTIVE: To evaluate the effectiveness of a new strategy for using artificial intelligence (AI) as supporting reader for the detection of breast cancer in mammography-based double reading screening practice. METHODS: Large-scale multi-site, multi-vendor data were used to retrospectively evaluate a new paradigm of AI-supported reading. Here, the AI served as the second reader only if it agrees with the recall/no-recall decision of the first human reader. Otherwise, a second human reader made an assessment followed by the standard clinical workflow. The data included 280 594 cases from 180 542 female participants screened for breast cancer at seven screening sites in two countries and using equipment from four hardware vendors. The statistical analysis included non-inferiority and superiority testing of cancer screening performance and evaluation of the reduction in workload, measured as arbitration rate and number of cases requiring second human reading. RESULTS: Artificial intelligence as a supporting reader was found to be superior or noninferior on all screening metrics compared with human double reading while reducing the number of cases requiring second human reading by up to 87% (245 395/280 594). Compared with AI as an independent reader, the number of cases referred to arbitration was reduced from 13% (35 199/280 594) to 2% (5056/280 594). CONCLUSION: The simulation indicates that the proposed workflow retains screening performance of human double reading while substantially reducing the workload. Further research should study the impact on the second human reader because they would only assess cases in which the AI prediction and first human reader disagree.

Budget impact analysis of a fixed-dose combination of fluticasone propionate and formoterol fumarate (FP/FORM) in a pressurized metered-dose inhaler (pMDI) for asthma.

INTRODUCTION: The economic burden of asthma on the UK National Health Service (NHS) is the largest among allergic diseases. Current asthma guidelines recommend adding a long acting ß2-agonist (LABA) to a low-dose inhaled corticosteroid (ICS) in patients who are on ICS monotherapy and have uncontrolled asthma. The fixed-dose combination of fluticasone propionate and salmeterol xinafoate (FP/SAL), available in a pressurized metered-dose inhaler (pMDI) device, is the most commonly prescribed ICS/LABA combination. An additional fixed-dose combination of fluticasone propionate and formoterol fumarate (FP/FORM) in pMDI is now available. In a 12-week non-inferiority study, FP/FORM demonstrated comparable efficacy to FP/SAL. The present analysis estimates the annual budget impact for the UK NHS using FP/FORM as an alternative to FP/SAL. METHODS: Current pMDI prescribing data were from a real-world UK patient database (Cegedim Strategic Data). Annual costs to the NHS for drug acquisition, administration, and monitoring were estimated for FP/FORM and FP/SAL and used to assess the potential budget impact for the NHS for the use of FP/FORM instead of FP/SAL. Varying rates of uptake, adherence, adverse event-related costs, and resource use associated with switching treatment were assessed in scenario analyses. RESULTS: Assuming similar levels of ICS use with both regimens, annual drug acquisition costs per person were lower with FP/FORM (£412) than with FP/SAL (£509). The difference in acquisition costs and otherwise comparable input costs between the treatments, results in potential annual savings of £15,110,279 to the NHS, assuming uptake of FP/FORM over FP/SAL in 50% of existing patients. The introduction of FP/FORM results in cost savings for the NHS in all of the assessed scenario analyses. CONCLUSIONS: The comparable efficacy and lower acquisition costs of FP/FORM compared with FP/SAL make it a cost-saving option for the UK NHS for the treatment of asthma patients requiring combination maintenance therapy using a pMDI.