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Autofuse is a software package that provides physicians a means for comparison of medical data including imaging data that is DICOM compliant. It allows the display, annotation, volume operation, volume rendering, registration and fusion of medical images as an aid during use by diagnostic radiology, oncology, radiation therapy planning, and other medical specialties. Autofuse is not intended for mammography.

Autofuse is a software application providing relevant tools for Radiotherapy professionals to use while creating treatment plans.

The Autofuse device is a Picture Archiving and Communication System (medical imaging software). Autofuse provides medical image processing designed to facilitate the oncology or other clinical specialty workflow by allowing the comparison of medical imaging data from different modalities, points in time, and / or scanning protocols. The product provides users with the means to display, co-register and analyze medical images from multiple modalities including PET, CT, RT Dose and MR; draw Regions of Interest (ROI); and import / export results to/from commercially available radiation treatment planning systems and PACS devices. Co-registration includes deformable registration technology that can be applied to DICOM data including diagnostic and planning image volumes, structures, and dose.

Autofuse is used as a stand-alone application on recommended Off-The-Shelf (OTS) computers supplied by the company or by the end-user.

Here's a breakdown of the acceptance criteria and study information for the "Autofuse" device, based on the provided FDA 510(k) submission:

Acceptance Criteria and Device Performance

The submission primarily focuses on demonstrating substantial equivalence to a predicate device ("Velocity AIS") by comparing technological characteristics and asserting similar or better performance based on non-clinical testing. There isn't a table explicitly listing "acceptance criteria" with numerical targets and then "reported device performance" against those targets in a quantitative sense for Autofuse itself. Instead, the "acceptance criteria" are implied by the features and performance of the predicate, and the "reported device performance" is a general statement of conformance based on software verification and validation.

Implied Acceptance Criteria and Reported Device Performance (Derived from Comparison to Predicate):

Study Information:

1. Sample sizes used for the test set and the data provenance:

   • The submission does not specify a distinct "test set" sample size in terms of number of cases or patients.
   • The non-clinical validation testing was performed in a "Staging Environment (SE), which consists of a network of virtual machines (VMs) within a Kernel Virtual Machine (KVM) hypervisor." This environment was "designed to mimic a typical clinical set up" and included a departmental CT scanner and a radiology PACS.
   • No information is provided regarding the country of origin of the data used for testing, nor whether it was retrospective or prospective. It's implied to be simulated or example data suitable for verification and validation.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

   • The submission does not mention the use of experts to establish ground truth for a test set. The validation is focused on software verification against specifications and intended use in a simulated environment, rather than clinical performance against expert-defined ground truth.

3. Adjudication method (e.g. 2+1, 3+1, none) for the test set:

   • No information on adjudication methods is provided, as the study described is software verification and validation, not a reader-based clinical study.

4. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:

   • No MRMC comparative effectiveness study was done or reported in this submission.

5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:

   • Yes, the primary performance data presented is "Non-clinical performance Data" based on "Software Verification and Validation Testing." This represents the standalone performance of the algorithm against its documented specifications and user needs in a simulated environment. The device is described as "a stand-alone application."

6. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):

   • For the non-clinical performance data, the "ground truth" is effectively the device specifications, user needs, and applicable requirements and standards against which the software was tested. It is not clinical ground truth like pathology or expert consensus on patient data.

7. The sample size for the training set:

   • The submission does not explicitly describe a "training set" or "training process" for the Autofuse software in the context of machine learning, as it is characterized as medical image management and processing software with deformable registration technology, rather than a deep learning-based diagnostic algorithm requiring extensive training data. Therefore, no sample size for a training set is provided.

8. How the ground truth for the training set was established:

   • As no training set is described in the context of machine learning, no information is provided on how ground truth for a training set was established.

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The Autofuser family of ambulatory infusion pumps with integrated administration set, either separately or as part of a convenience kit, is intended for general infusion use. Routes of administration include intravenous, percutaneous, subcutaneous, intra-arterial and epidural, and into intra-operative (soft tissue/body cavity) sites.

Within the Autofuser family are pump models intended for patient-controlled infusion using the integrated bolus button.

General infusion uses include continuous infusion of a local anesthetic near a nerve for regional anesthesia and pain management for pre-operative, perioperative and postoperative surgery.

The modification to the existing device is the addition of new flow rates, new reservoir size and procedure kit components.

Here's an analysis of the provided text regarding the Ace Medical Autofuser System (K060258), broken down by your requested categories:

1. Table of Acceptance Criteria and Reported Device Performance

The provided text does not explicitly list specific numerical acceptance criteria for the device's performance (e.g., "flow rate must be within X% of target"). Instead, it states that:

From the text, "The design verification and validation activities have been performed and the results demonstrate that the predetermined acceptance criteria were met," implies that internal criteria were established and achieved, but the specific metrics are not detailed in this summary.

2. Sample Size Used for the Test Set and Data Provenance

The provided document does not specify a sample size for any test set or provide details on data provenance (e.g., country of origin, retrospective/prospective). The submission focuses on modifications to an existing device and demonstrating substantial equivalence through design verification and validation, rather than a clinical study with a patient-based test set.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications

The document does not mention the use of experts to establish ground truth for a test set. The evaluation appears to be based on engineering verification and validation of the device's performance against internal specifications and comparison to predicate devices, not on expert consensus regarding clinical outcomes.

4. Adjudication Method for the Test Set

As no test set involving human assessment or expert review is described, there is no adjudication method mentioned.

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

The document does not describe an MRMC comparative effectiveness study. This submission is for modifications to an elastomeric infusion pump, which typically does not involve this type of study design. The focus is on the device's mechanical and functional performance, not on human interpretive tasks that would benefit from MRMC studies.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

The device is an elastomeric infusion pump. There is no "algorithm only" or "standalone" performance described in the context of AI or software. The performance assessed would be the physical pumping mechanism and its ability to deliver fluids at specific rates.

7. Type of Ground Truth Used

The "ground truth" for this device's performance is implicit in its design specifications and functional requirements. For example, a target flow rate, reservoir size, and bolus delivery mechanism. The document states that "design verification and validation activities have been performed and the results demonstrate that the predetermined acceptance criteria were met." This implies the ground truth was based on engineering specifications and established performance benchmarks for infusion pumps, not on pathology, outcomes data, or expert consensus in a clinical sense.

8. Sample Size for the Training Set

As this is a submission for a medical device (an elastomeric infusion pump) and not an AI/ML algorithm that requires a training set, the concept of a "training set" does not apply to this document.

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

Since there is no training set for an AI/ML algorithm for this device, information on how its "ground truth" was established is not applicable.

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The AutoFuser family of ambulatory infusion pumps with integrated administration set, either separately or as part of a convenience kit, are indicated for general infusion use. Routes of infusion include intravenous, percutaneous, subcutaneous, inter-arterial and epidural, and into intra-operative (soft tissue / body cavity) sites.

Within the AutoFuser family are pump models intended for patient-controlled infusion using the integrated bolus button.

General infusion uses include continuous infusion of a local anesthetic near a nerve for regional anesthesia and pain management for pre-operative, perioperative and postoperative surgery.

The AutoFuser pump (Continuous type Silicone Balloon) provides continuous fluid delivery with attached, fixed rate administration set. The pumps are supplied as fixed flow rates. A silicone balloon is used as both the fluid reservoir of the device and the pressure (energy) source.

The AutoFuser pump family includes models of the standard AutoFuser with the addition of a Patient Medication Control Module (PCM). The Patient Medication Control Module allows the patient to administer a bolus of fixed volume with a fixed lockout (re-fill) time.

The pump is a disposable device intended for single patient use. The pump is suitable for use as an ambulatory device and is intended for use in the hospital, home environment or alternative care sites.

The AutoFuser and the AutoFuser PCM pumps are substantially similar to the I-Flow Homepump (Eclipse) C-Series (included in the I-Flow PainBuster kit and the On-Q pain management kit), the Baxter Infuser and Intermate, the McKinley Accufuser, and the B Braun spring pump (used in the Sgarlato pain kit).

The AutoFuser and the AutoFuser PCM pumps have fill volumes and flow rates substantially similar to the pumps of the McKinley Medical Accufuser, the I-Flow PainBuster (Homepump Eclipse C-Series), the B Braun spring pump included in the Sgarlato PCIP, and the Baxter Infusor and Intermate pumps.

The AutoFuser and AutoFuser PCM pumps and the identified predicate device use either a glass orifice or PVC tubing to control the flow rate.

All fluid path materials of the AutoFuser pumps are in conformance with ISO 10993 Part 1.

The provided text is a 510(k) premarket notification for a medical device called the "AutoFuser Elastomeric Infusion Pump." This document primarily focuses on demonstrating substantial equivalence to predicate devices for regulatory clearance, rather than presenting a detailed study proving the device meets specific acceptance criteria with performance metrics, ground truth, or statistical analyses common in AI/ML device studies.

Therefore, many of the requested sections regarding acceptance criteria, study details, sample sizes, expert qualifications, and ground truth establishment cannot be directly extracted or inferred from this document.

However, I can provide the information that is available:

1. Table of Acceptance Criteria and Reported Device Performance

As this is a 510(k) summary for an elastomeric infusion pump, the "acceptance criteria" are primarily related to substantial equivalence to predicate devices and meeting general device requirements rather than specific performance metrics like sensitivity, specificity, or accuracy (which are common for diagnostic AI/ML devices). The document states the device is "substantially similar" to predicate devices in several aspects.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Not Applicable / Not Provided. This document does not describe a clinical study with a test set in the way an AI/ML device submission would. The submission relies on demonstrating substantial equivalence to existing, legally marketed predicate devices through comparison of design, functionality, materials, and intended use. Performance testing related to flow accuracy and other physical parameters would have been conducted by the manufacturer, but the details (sample size, data provenance) are not part of this summary.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

• Not Applicable / Not Provided. This type of information is relevant for studies validating diagnostic algorithms against expert consensus or pathology. For an infusion pump, "ground truth" would relate to precise fluid delivery, which is typically measured mechanically or chemically, not by human experts.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

• Not Applicable / Not Provided. Adjudication methods are used in studies involving subjective expert review, typically in diagnostic or qualitative assessments. This is not relevant for the type of device and submission described.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

• Not Applicable / Not Provided. MRMC studies are specific to evaluating diagnostic systems, particularly those that involve human interpretation assisted by AI. The AutoFuser is an infusion pump, a therapeutic device, and does not involve "human readers" or AI assistance in this context.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

• Not Applicable / Not Provided. This question is also focused on AI/ML algorithms. The AutoFuser is a mechanical/elastomeric device, not an algorithm. Its performance is inherent to its design and manufacturing, not an algorithm's output.

7. The type of ground truth used (expert concensus, pathology, outcomes data, etc)

• Not Applicable / Not Provided (for the document content). For an infusion pump, "ground truth" for performance would be established through engineering and bench testing. This would involve precise measurements of flow rate, fill volume, pressure, and material compatibility against established standards and specifications. The document states "All fluid path materials of the AutoFuser pumps are in conformance with ISO 10993 Part 1," indicating adherence to material safety standards. However, the details of flow rate accuracy testing are not described in this summary.

8. The sample size for the training set

• Not Applicable / Not Provided. This refers to AI/ML model training data. This device is not an AI/ML product.

9. How the ground truth for the training set was established

• Not Applicable / Not Provided. This refers to AI/ML model training data. This device is not an AI/ML product.

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