Tag: radiography

AI for Chest X-Ray Varies

Posted on by Brian Casey

Not all AI is created equal when it comes to analyzing chest X-rays. A new study in Radiology found wide variation in performance for seven commercially available chest X-ray algorithms to detect lung cancer. 

X-ray is by far the most widely used imaging modality. Radiography is often the first imaging exam a patient receives, and it frequently serves as a gateway to other more advanced imaging modalities. 

  • But radiography also has well-known shortcomings (which is why advanced imaging is needed for follow-up). Could AI help unlock X-ray’s value and make it more useful?

That’s what a host of AI algorithm developers are banking on, but the wide variety of solutions can create confusion for clinicians.

  • So U.K. researchers decided to hold an AI bake-off, comparing commercially available algorithms from seven developers for detecting lung cancer on chest X-rays. 

The competing companies included Annalise/Harrison.ai, Gleamer, Infervision, Milvue, Oxipit, Qure.ai, and Rayscape. Researchers anonymized performance results from the different products.

In all, chest radiographs from a dataset of 5.2k patients with a real-world lung cancer prevalence rate were included, with researchers finding…

  • Significant variance in algorithm performance by each of the major accuracy measures: sensitivity (21%-78%), specificity (59%-98%), and positive predictive value (1.5%-28%). 
  • All the algorithms increased the number of false positives, and with significant variation. One model generated only 10 more false positives than radiologists, while another produced – wait for it – over 2k. 
  • If used to triage patients for follow-up CT exams, one model would generate $1.6k in additional costs while another would produce $327k.

What accounts for the variation? An underlying factor is most likely differences in the datasets used for model training. 

  • In any event, the study underscores the need for more head-to-head comparisons to determine the strengths and weaknesses of individual AI algorithms. 

The Takeaway

This week’s study on how AI performance varies between commercially available algorithms initially seems disturbing and might suggest a need for stronger regulatory oversight. But AI’s diversity could be its strength in a future where every patient case is analyzed by multiple different algorithms, each with its own advantages. This could ultimately produce a more complete picture of the patient than any one algorithm on its own.

AI for Bone Density Screening with X-Ray

Posted on by Brian Casey

Screening women for osteoporosis using AI analysis of chest X-rays acquired for other clinical indications meets U.S. thresholds for cost-effectiveness. That’s according to a new study in JACR that highlights the potential of radiography AI for opportunistic screening.

Osteoporosis screening is already performed using DEXA scanners that detect bone density loss in women.

  • But DEXA scanners aren’t always available, and dedicated screening for just one condition can be expensive. 

Using AI to analyze chest X-rays that women might be getting for other conditions could expand the pool of women being screened for osteoporosis without incurring significant additional costs.

  • Indeed, Japanese researchers recently published a study honing in on the best techniques for AI-enhanced osteoporosis screening with radiography.

In the new study, researchers performed a modeling analysis that simulated the cost-effectiveness of an osteoporosis screening program based on AI-enhanced chest radiographs for U.S. women aged 50 and up. 

  • The cost analysis compared osteoporosis screening plus treatment versus treatment alone, incorporating standard fracture treatment and imaging costs ($66 for DEXA scans, $20 for chest X-rays).

In a sample of 1k women, AI-enhanced X-ray osteoporosis screening…

  • Had an ICER of $72.1k per QALY, below the U.S. cost-effectiveness thresholds of $100k to $150k per QALY.
  • Would produce healthcare savings of $99k, offset by treatment costs of $208k.
  • Would prevent 2.8 fractures and increase QALYs by 1.5.
  • Would remain cost-effective as long as AI’s cost did not exceed $62 per patient.

Adjusting the model’s parameters produced even better performance for AI-based screening. 

  • If medication adherence improved by 50%, the ICER was reduced to $28.6k.

The Takeaway

The new research offers more support for opportunistic osteoporosis screening, this time perhaps from the most important angle of all: cost-effectiveness. If confirmed with other studies, AI-based bone density analysis could make routine chest X-rays even more valuable.

Time to Embrace X-Ray AI for Early Lung Cancer Detection

Posted on by Partner Content

Each year approximately 2 billion chest X-rays are performed globally. They are fast, noninvasive, and a relatively inexpensive radiological examination for front-line diagnostics in outpatient, emergency, or community settings. 

  • But beyond the simplicity of CXR lies a secret weapon in the fight against lung cancer: artificial intelligence. 

Be it serendipitous screening, opportunistic detection, or incidental identification, there is potential for AI incorporated into CXR to screen patients for disease when they are getting an unrelated medical examination. 

  • This could include the patient in the ER undergoing a CXR for suspected broken ribs after a fall, or an individual referred by their doctor for a CXR with suspected pneumonia. These people, without symptoms, may unknowingly have small yet growing pulmonary nodules. 

AI can find these abnormalities and flag them to clinicians as a suspicious finding for further investigation. 

  • This has the potential to find nodules earlier, in the very early stages of lung cancer when it is easier to biopsy or treat. 

Indeed, only 5.8% of eligible ex-smoking Americans undergo CT-based lung cancer screening. 

  • So the ability to cast the detection net wider through incidental pulmonary nodule detection has significant merits. 

Early global studies into the power of AI for incidental pulmonary nodules (IPNs) shows exciting promise.

  • The latest evidence shows one lung cancer detected for every 1,120 CXRs has major implications to diagnose and treat people earlier – and potentially save lives. 

The qXR-LN chest X-ray AI algorithm from Qure.ai is raising the bar for incidental pulmonary nodule detection. In a retrospective study performed on missed or mislabelled US CXR data, qXR-LN achieved an impressive negative predictive value of 96% and an AUC score of 0.99 for detection of pulmonary nodules. 

  • By acting as a second pair of eyes for radiologists, qXR-LN can help detect subtle anatomical anomalies that may otherwise go unnoticed, particularly in asymptomatic patients.

The FDA-cleared solution serves as a crucial second reader, assisting in the review of chest radiographs on the frontal projection. 

  • In another multicenter study involving 40 sites from across the U.S., the qXR-LN algorithm demonstrated an impressive AUC of 94% for scan-level nodule detection, highlighting its potential to significantly impact patient outcomes by identifying early signs of lung cancer that can be easily missed. 

The Takeaway 

By harnessing the power of AI for opportunistic lung cancer surveillance, healthcare providers can adopt a proactive approach to early detection, without significant new investment, and ultimately improving patient survival rates.

Qure.ai will be exhibiting at RSNA 2024, December 1-4. Visit booth #4941 for discussion, debate, and demonstrations.

Sources

AI-based radiodiagnosis using Chest X-rays: A review. Big Data Analytics for Social Impact, Volume 6 – 2023

Results from a feasibility study for integrated TB & lung cancer screening in Vietnam, Abstract presentation UNION CONF 2024: 2560   

Performance of a Chest Radiography AI Algorithm for Detection of Missed or Mislabelled Findings: A Multicenter Study. Diagnostics 12, no. 9 (2022): 2086

Qure.ai. Qure.ai’s AI-Driven Chest X-ray Solution Receives FDA Clearance for Enhanced Lung Nodule Detection. Qure.ai, January 7, 2024

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