Critical Care Suite is a computer aided triage and notification device that analyzes frontal chest x-ray images for the presence of prespecified critical findings (pneumothorax). Critical Care Suite identifies images with critical findings to enable case prioritization or triage in the PACS/workstation.

Critical Care Suite is intended for notification only and does not provide diagnostic information beyond the notification. Critical Care Suite should not be used in-lieu of full patient evaluation or solely relied upon to make or confirm a diagnosis. It is not intended to replace the review of the x-ray image by a qualified physician.

Critical Care Suite is indicated for adult-size patients.

Device Description

Critical Care Suite is a software module that employs Al-based image analysis algorithms to identify pre-specified critical findings (pneumothorax) in frontal chest X-ray images and flag the images in the PACS/workstation to enable prioritized review by the radiologist.

Critical Care Suite employs a sequence of vendor and system agnostic AI algorithms to ensure that the input images are suitable for the pneumothorax detection algorithm and to detect the presence of pneumothorax in frontal chest X-rays:

The Quality Care Suite algorithms conduct an automated check to confirm that the input image is compatible with the pneumothorax detection algorithm and that the lung field coverage is adequate;
the PTX Classifier determines whether a pneumothorax is present in the image.

If a pneumothorax is detected, Critical Care Suite enables case prioritization or triage through direct communication of the Critical Care Suite notification during image transfer to the PACS. It can also produce a Secondary Capture DICOM Image that presents the Al results to the radiologist.

When deployed on a Digital Projection Radiographic Systems such as Optima XR240amx, Critical Care Suite is automatically run after image acquisition. Quality Care Suite algorithms produce an on-device notification if the lung field has atypical positioning to give the technologist the opportunity to make correction before sending the image to the PACS. The Critical Care Suite output is then sent directly to PACS upon exam closure where images with a suspicious finding are flagged for prioritized review by the Radiologist.

In parallel, an on-device, technologist notification is generated 15 minutes after exam closure, indicating which cases were prioritized by Critical Care Suite in PACS. The technologist notification is contextual and does not provide any diagnostic information. The on-device, technologist notification is not intended to inform any clinical decision, prioritization, or action.

The Digital Projection Radiographic System intended use remains unchanged in that the system is used for general purpose diagnostic radiographic imaging.

AI/ML Overview

Here's a breakdown of the acceptance criteria and the study that proves the device meets them, based on the provided text:

Acceptance Criteria and Device Performance

1. Table of Acceptance Criteria and Reported Device Performance:

Metric	Acceptance Criteria (Predicate Device HealthPNX - K190362)	Critical Care Suite Reported Performance
ROC AUC	> 0.95	0.9607 (95% CI [0.9491, 0.9724])
Specificity	93%	93.5% (95% CI [91.1%, 95.8%])
Sensitivity	93%	84.3% (95% CI [80.6%, 88.0%])
AUC on large pneumothorax	Not assessed	0.9888 (95% CI [0.9810, 0.9965])
Sensitivity on large pneumothorax	Not assessed	96.3% (95% CI [93.3%, 99.2%])
AUC on small pneumothorax	Not assessed	0.9389 (95% CI [0.9209, 0.9570])
Sensitivity on small pneumothorax	Not assessed	75% (95% CI [69.2%, 80.8%])
Timing of notification (delay in PACS worklist)	22 seconds (HealthPNX)	No delay (immediately on PACS receipt)

2. Sample size and Data Provenance for the Test Set:

Sample Size: 804 frontal chest X-ray images (N=376 for pneumothorax present; N=428 for pneumothorax absent).
Data Provenance: Collected in North America, representative of the intended population. The text does not explicitly state if it was retrospective or prospective, but the nature of a "collected dataset" for evaluation typically implies retrospective analysis of existing images.

3. Number of Experts and Qualifications for Ground Truth of the Test Set:

Number of Experts: 3 independent US-board certified radiologists.
Qualifications: "US-board certified radiologists." No specific years of experience or subspecialty are mentioned beyond board certification.

4. Adjudication Method for the Test Set:

The text states the ground truth was "established by 3 independent US-board certified radiologists." It does not explicitly detail a specific adjudication method like 2+1 or 3+1. This implies a consensus-based approach where the radiologists independently reviewed images to establish the ground truth, likely resolving discrepancies through discussion to reach a final determination.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

No, a multi-reader multi-case (MRMC) comparative effectiveness study directly comparing human readers with AI assistance vs. without AI assistance was not reported in this summary. The study focused on the standalone diagnostic performance of the AI algorithm.

6. Standalone (Algorithm Only) Performance Study:

Yes, a standalone performance study of the algorithm without human-in-the-loop was done. The reported metrics (ROC AUC, Sensitivity, Specificity) are direct measurements of the algorithm's performance against the established ground truth.

7. Type of Ground Truth Used:

Expert Consensus: The ground truth was established by "3 independent US-board certified radiologists." This indicates an expert consensus approach.

8. Sample Size for the Training Set:

The document does not explicitly state the sample size for the training set. It mentions the algorithm was "trained on annotated medical images" but provides no further details on the quantity of images used for training.

9. How the Ground Truth for the Training Set Was Established:

The document states the device utilizes a "deep learning algorithm trained on annotated medical images." While it doesn't explicitly describe the method for establishing ground truth for the training set, it is implied that these "annotated medical images" had pre-existing labels or were labeled by experts for the purpose of training the AI.

Summary

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August 12, 2019

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GE Medical Systems, LLC. % Camille Vidal Director of Regulatory Affairs Strategy 3000 N. Grandview Blvd. WAUKESHA WI 53188

Re: K183182

Trade/Device Name: Critical Care Suite Regulation Number: 21 CFR 892.2080 Regulation Name: Radiological computer aided triage and notification software Regulatory Class: Class II Product Code: QFM Dated: July 12, 2019 Received: July 12, 2019

Dear Camille Vidal:

We have reviewed your Section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database located at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmp/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.

If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.

Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR 803) for devices or postmarketing safety reporting (21 CFR 4, Subpart B) for combination products (see

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https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.

Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.

For comprehensive regulatory information about medical devices and radiation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).

Sincerely,

For

Thalia T. Mills, Ph.D. Director Division of Radiological Health OHT7: Office of In Vitro Diagnostics and Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health

Enclosure

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Indications for Use

510(k) Number (if known) K183182

Device Name Critical Care Suite

Indications for Use (Describe)

Critical Care Suite is intended for notification only and does not information beyond the notification. Critical Care Suite should not be used in-lieu of full patient evaluation or solely relied upon to make or confirm a diagnosis. It is not intended to replace the review of the x-ray image by a qualified physician.

Critical Care Suite is indicated for adult-size patients.

X Prescription Use (Part 21 CFR 801 Subpart D)

Over-The-Counter Use (21 CFR 801 Subpart C)

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510(k) Summary K183182

In accordance with 21 CFR 807.92 the following summary of information is provided:

Date:	August 7th, 2019
Submitter:	GE Medical Systems, LLC3000 N. Grandview BlvdWaukesha, WI 53188, USA
Primary Contact Person:	Camille VidalDirector of Regulatory Affairs StrategyGE Healthcare240-280-5356Camille.Vidal@ge.com
Secondary Contact Person:	Diane UriellRegulatory Affairs DirectorGE Healthcare262-290-8218Diane.Uriell@ge.com
Device Trade Name:	Critical Care Suite
Common/Usual Name:	Radiological Computer Assisted Triage and Notification Software
Classification Names:	Class II, Radiological Computer Assisted Triage and Notification Software,21 CFR 892.2080
Product Code:	QFM
Predicate Device(s):	HealthPNX by Zebra Medical Vision, K190362Class II, 21 CFR 892.2080, Product code: QFM
Indications for use	Critical Care Suite is a computer aided triage and notification device thatanalyzes frontal chest x-ray images for the presence of prespecifiedcritical findings (pneumothorax). Critical Care Suite identifies images withcritical findings to enable case prioritization or triage in thePACS/workstation.Critical Care Suite is intended for notification only and does not providediagnostic information beyond the notification. Critical Care Suite shouldnot be used in-lieu of full patient evaluation or solely relied upon to makeor confirm a diagnosis. It is not intended to replace the review of the x-ray image by a qualified physician.Critical Care Suite is indicated for adult-size patients.
DeviceDescription:	Critical Care Suite is a software module that employs Al-based imageanalysis algorithms to identify pre-specified critical findings(pneumothorax) in frontal chest X-ray images and flag the images in thePACS/workstation to enable prioritized review by the radiologist.Critical Care Suite employs a sequence of vendor and system agnostic AIalgorithms to ensure that the input images are suitable for thepneumothorax detection algorithm and to detect the presence ofpneumothorax in frontal chest X-rays:- The Quality Care Suite algorithms conduct an automated checkto confirm that the input image is compatible with thepneumothorax detection algorithm and that the lung fieldcoverage is adequate;- the PTX Classifier determines whether a pneumothorax ispresent in the image.If a pneumothorax is detected, Critical Care Suite enables caseprioritization or triage through direct communication of the Critical CareSuite notification during image transfer to the PACS. It can also producea Secondary Capture DICOM Image that presents the Al results to theradiologist.When deployed on a Digital Projection Radiographic Systems such asOptima XR240amx, Critical Care Suite is automatically run after imageacquisition. Quality Care Suite algorithms produce an on-devicenotification if the lung field has atypical positioning to give the
technologist the opportunity to make correction before sending theimage to the PACS. The Critical Care Suite output is then sent directly to
PACS upon exam closure where images with a suspicious finding areflagged for prioritized review by the Radiologist.
In parallel, an on-device, technologist notification is generated 15 minutesafter exam closure, indicating which cases were prioritized by Critical CareSuite in PACS. The technologist notification is contextual and does notprovide any diagnostic information. The on-device, technologistnotification is not intended to inform any clinical decision, prioritization,or action.
The Digital Projection Radiographic System intended use remainsunchanged in that the system is used for general purpose diagnosticradiographic imaging.

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GE Healthcare 510(k) Premarket Notification Submission

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Predicate Device Comparison	Critical Care Suite	HealthPNX (K190362)
Device classification	Radiological Computer Assisted Triage and Notification software, Class II, QFM	Radiological Computer Assisted Triage and Notification software, Class II, QFM
Targeted clinical condition, anatomy and modality	PneumothoraxChest/LungFrontal Chest X-ray	PneumothoraxChest/LungChest X-ray
Input Validation	Quality Care Suite algorithms conduct an automated check to confirm image is compatible with processing algorithm (age, frontal chest, lung field)Atypical lung field positioning generates notifications on the X-ray system and Secondary Capture DICOM Image when generated.	Validation feature of HealthPNX verifies that input age, modality and view to ensure compatibility with processing algorithm.In case of failure during data validation, system outputs an error code.
Algorithm for Pneumothorax detection	AI algorithm designed to detect pneumothorax in frontal chest X-ray imagesCritical Care Suite uses a vendor agnostic algorithm compatible with DICOM frontal chest X-ray images acquired on fixed or mobile systems.	AI algorithm designed to detect pneumothorax in chest X-ray images.HealthPNX employs a vendor agnostic algorithm compatible with DICOM chest X-ray images.
Computational Platform	Critical Care Suite is designed as a software module that can be deployed on several computing and X-ray imaging platforms such as Digital Projection Radiographic Systems, PACS. On Premise or On Cloud.	Cloud-based computation upon transfer to PACS of image

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GE Healthcare 510(k) Premarket Notification Submission

Predicate DeviceComparison	Critical Care Suite	HealthPNX (K190362)
Device output in caseof positive detection	Critical Care Suite enables case prioritizationor triage through direct communication of theCritical Care Suite notification during imagetransfer to the PACS.	Integration module notifies thePACS/workstation for prioritization throughthe worklist interface.
	No markup on original image	No markup on original image
	Upon image acquisition on a Digital ProjectionRadiographic System, an on-device,technologist notification is generated 15minutes after exam closure, indicating whichcases were prioritized by Critical Care Suite inPACS. The technologist notification iscontextual and does not provide anydiagnostic information. The on-device,technologist notification is not intended toinform any clinical decision, prioritization, oraction.
Notification:Recipient, timing andmeans of notification	Passive notification to radiologist. Images withsuspicion of pneumothorax are flagged inPACS/workstation.	Passive notification to radiologist. Images withsuspicion of pneumothorax are flagged inPACS/workstation.
Performance level -timing of notification	Exams arrive on PACS with the passivenotification already incorporated, thereforethere is no delay for image transfer orcomputation. The worklist prioritizationhappens immediately once the exam isreceived on the PACS.	Passive notification is visible upon transfer tothe PACS with a delay of about 22 seconds forimage transfer to the cloud, computation andresults transfer.
Performance level -accuracy ofclassification	ROC AUC > 0.95AUC: 0.9607 (95% CI [0.9491, 0.9724])Specificity 93.5% (95% CI [91.1%, 95.8%])Sensitivity 84.3% (95% CI [80.6%, 88.0%])AUC on large pneumothorax 0.9888 (95% CI[0.9810, 0.9965])Sensitivity on large pneumothorax 96.3% (95%CI [93.3%, 99.2%])AUC on small pneumothorax 0.9389 (95% CI[0.9209, 0.9570])Sensitivity on small pneumothorax 75% (95%CI [69.2%, 80.8%])	ROC AUC > 0.95AUC: 0.983 (95% CI [0.9740, 0.9902]),Specificity: 93%Sensitivity: 93%Stratified results on small vs. largepneumothorax not assessed.

Clinical and Non-Clinical Tests	Summary of Non-Clinical Tests:
	Critical Care Suite contains a set of AI algorithms that resides within asoftware module that has been designed to be integrated within severalcomputing and imaging platforms, such as the Optima XR240amx.
The following quality assurance measures were applied to thedevelopment of Critical Care Suite and deployment onto the OptimaXR240amx system:
■ Risk Analysis
■ Requirements Reviews
■ Design Reviews
■ Testing on unit level (Module verification)
■ Integration testing (System verification)
■ Performance testing (Verification)
■ Safety testing (Verification)
■ Simulated use testing (Validation)
Critical Care Suite specific verification was conducted to demonstrateproper implementation of Critical Care Suite software designrequirements.
Regression testing of the Optima XR 240amx feature functionality wasconducted to verify proper integration of the Critical Care Suite into theOptima XR240amx software and device. Validation was performed onOptima XR240amx with integrated Critical Care Suite.
The test plans have been executed with acceptable results.
Timing Performance
Internal bench testing was conducted with and without Critical CareSuite integrated within the Optima XR240amx. The average time toacquire, annotate, process and transfer an image from the x-ray systemto PACS was measured and found to take 42 seconds on average.Whether Critical Care Suite was on or off did not make a statisticaldifference in the timing. This shows that Critical Care Suite has no timingimpact on image acquisition, processing, annotation and transfer toPACS as compared to standard of care when measured in the sameconditions.
Since the image arrives on PACS with the passive notification alreadyincorporated, the worklist prioritization happens immediately once theimage is received on the PACS.
	The timing of the processing and prioritization is well within the clinicaloperational expectations of standard chest radiographic exam and itsreading by radiologists.
	According to Gaskin, Cree M., et al. "Impact of a Reading Priority ScoringSystem on the Prioritization of Examination Interpretations." AmericanJournal of Roentgenology 206.5 (2016): 1031-1039 and Rachh, Pratik, etal. "Reducing STAT Portable Chest Radiograph Turnaround Times: A PilotStudy." Current problems in diagnostic radiology (2017), the estimatedaverage Report Turnaround Time for non-prioritized or ineffectivelyprioritized exams is between 7.23 hours and 8.67 hours.
	Incorporating the Critical Care Suite passive notification to helpradiologists prioritize their exam reads would drastically reduce thisturnaround time for the cases that have been flagged by Critical CareSuite as compared to standard of care (First-In, First-Out).
	Summary of Clinical Evaluation:
	Critical Care Suite was evaluated on a dataset of 804 frontal chest X-rayscollected in North America and representative of the intendedpopulation. The algorithm prediction is compared to the ground truthestablished by 3 independent US-board certified radiologists. Thealgorithm ROC AUC meets the performance requirement of FDA productcode QFM (AUC>95%): AUC=96% (95% CI [94.9% - 97.2%]) (PTX present:N=376; PTX absent: N=428). Stratified analyses showed consistentperformance across image view (AP/PA), system manufacturer (GE/non-GE) and data sources.
	Critical Care Suite performs at high specificity 93.5% (95% CI [91.1% -95.8%]) and high sensitivity 84.3% (95% CI [80.6% – 88.0%]). Stratifiedanalysis by pneumothorax size shows that nearly all largepneumothoraces are detected (96.3% with 95% CI [93.3% - 99.2%])while 3 out 4 small pneumothoraces are detected (75% with 95% CI[69.2% - 80.8%]) with limited false notifications thanks to the highspecificity.
SubstantialEquivalenceDiscussion:	Critical Care Suite and HealthPNX are software devices intended to aid intriage and prioritization of radiological images. Both devices use artificialintelligence algorithms to identify suspicious findings suggestive ofpneumothorax in chest X-ray images. Both devices are intended to
notify the radiologist by producing a passive notification in the form of acase level flag in the PACS/workstation.
Critical Care Suite, when deployed on a Digital Projection RadiographicSystem, generates an on-device, technologist notification 15 minutesafter exam closure, indicating which cases were prioritized by CriticalCare Suite in PACS. The technologist notification is contextual and doesnot provide any diagnostic information. The on-device, technologistnotification is not intended to inform any clinical decision, prioritization,or action.
The predicate and proposed devices use similar artificial intelligencetechniques to process radiological images. Specifically, the proposed andpredicate software utilize a deep learning algorithm trained onannotated medical images. Both trained algorithms achieve the highaccuracy performance requirement for product code QFM (ROC AUC>0.95) in the detection of pneumothorax in a representative imagedataset withheld for testing. Critical Care Suite and HealthPNX operatesat high specificity and high sensitivity.
Critical Care Suite was shown to have no impact on timing of imageacquisition, processing, annotation and transfer to PACS as compared tostandard of care. Exams arrive on PACS with the passive notificationalready incorporated, therefore there is no delay for image transfer orcomputation. The worklist prioritization happens immediately once theexam is received on the PACS. In comparison, HealthPNX, reports a delayof about 22 seconds for results to appear in the PACS worklist.The differences between Critical Care Suite and HealthPNX do not raise
new type of safety and effectiveness question.Non-clinical and clinical testing shows that Critical Care Suite deliverseffective triage by accurately detecting cases with pneumothorax andgenerating a passive notification in the PACS/workstation as soon as theimage is available for review in the PACS. Radiologists can easily identifyimages that will benefit from prioritized review, leading to reduced turn-around time.
Critical Care Suite was found to be substantially equivalent toHealthPNX.

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Regulation Number and Section

§ 892.2080 Radiological computer aided triage and notification software.

(a)
Identification. Radiological computer aided triage and notification software is an image processing prescription device intended to aid in prioritization and triage of radiological medical images. The device notifies a designated list of clinicians of the availability of time sensitive radiological medical images for review based on computer aided image analysis of those images performed by the device. The device does not mark, highlight, or direct users' attention to a specific location in the original image. The device does not remove cases from a reading queue. The device operates in parallel with the standard of care, which remains the default option for all cases.(b)
Classification. Class II (special controls). The special controls for this device are:(1) Design verification and validation must include:
(i) A detailed description of the notification and triage algorithms and all underlying image analysis algorithms including, but not limited to, a detailed description of the algorithm inputs and outputs, each major component or block, how the algorithm affects or relates to clinical practice or patient care, and any algorithm limitations.
(ii) A detailed description of pre-specified performance testing protocols and dataset(s) used to assess whether the device will provide effective triage (
e.g., improved time to review of prioritized images for pre-specified clinicians).(iii) Results from performance testing that demonstrate that the device will provide effective triage. The performance assessment must be based on an appropriate measure to estimate the clinical effectiveness. The test dataset must contain sufficient numbers of cases from important cohorts (
e.g., subsets defined by clinically relevant confounders, effect modifiers, associated diseases, and subsets defined by image acquisition characteristics) such that the performance estimates and confidence intervals for these individual subsets can be characterized with the device for the intended use population and imaging equipment.(iv) Stand-alone performance testing protocols and results of the device.
(v) Appropriate software documentation (
e.g., device hazard analysis; software requirements specification document; software design specification document; traceability analysis; description of verification and validation activities including system level test protocol, pass/fail criteria, and results).(2) Labeling must include the following:
(i) A detailed description of the patient population for which the device is indicated for use;
(ii) A detailed description of the intended user and user training that addresses appropriate use protocols for the device;
(iii) Discussion of warnings, precautions, and limitations must include situations in which the device may fail or may not operate at its expected performance level (
e.g., poor image quality for certain subpopulations), as applicable;(iv) A detailed description of compatible imaging hardware, imaging protocols, and requirements for input images;
(v) Device operating instructions; and
(vi) A detailed summary of the performance testing, including: test methods, dataset characteristics, triage effectiveness (
e.g., improved time to review of prioritized images for pre-specified clinicians), diagnostic accuracy of algorithms informing triage decision, and results with associated statistical uncertainty (e.g., confidence intervals), including a summary of subanalyses on case distributions stratified by relevant confounders, such as lesion and organ characteristics, disease stages, and imaging equipment.