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The Codonics Safe Label System (SLS) and SLS Software provides a simple computerbased bar code scanning & printing system to automatically verify drug identity from NDC and other drug vial UDI Barcodes, and to print labels for prepared drugs and other items in use on patients during surgical procedures.

Codonics Safe Label System (SLS) is generally placed in, however not limited to, the perioperative environment to identify syringes prepared for anesthesiology use during surgery. Additional uses include producing labels for IVs and other artifacts used during a surgical procedure. SLS can also be used to print "non-surgical environment" color & text labels as required. Typical users of this system are trained professionals, including but not limited to physicians, nurses, and technicians

Codonics Safe Labeling System is a simple, integrated system utilizing a bar code scanner to read and confirm drug identity from NDC and other drug ID Barcodes from vials automatically print labels for prepared drugs and other items in use on patients during surgical procedures. The labels are compliant with national regulations focused on improving medication safety in the perioperative environment.

The software components provide functions for scanning vials, creating, indexing, and approved hospital managed promotion of a formulary database, displaying on screen and audibly confirming drug type, printing color The Joint Commission ISO and ASTM compliant labels with 2-D barcodes. The system reads drug vial barcodes and produces waterproof, color labels.

The system can be integrated to function with AIMS system workflow to provide real-time documentation of drug administration when the syringe "2D Barcode" is read.

Codonics Safe Labeling System consists of :

• PC (x86 Pico-ITX based w/on-board RAM) with Ethernet interface .
• . SSD/Flash disk
• Touch screen end-user interface (for configuring and controlling authorized users, . barcode scan and print jobs, selection of label type and manual/automatic label production % dilution factor indication)
• Barcode scanner/decoder .
• Label printing engine .
• Medical grade (EN60601-1) compliant power supply .

Software to support primary functions:

• Linux OS for CPU, I/O (USB, IDE or SATA, AUDIO, Network (Ethernet/WIFI), SSD . Disk access, and Touch Screen LCD display
• Specific Drivers for Touch Screen LCD, Ink Jet label printer, Barcode scanner, . Ethernet/WIFI communications
• NDC or other UDI (Unique Drug Identifier, commonly known in the US as an "NDC") . drug formulary persistent repository (or database) via SSD/USB Flash

In addition to these primary functions, there are other functions that are provided by the software, to include:

• System configuration (network, security, profiles, etc.) .
• Security management to perform accounting and authentication of user data; add . users to the User Database when requested and verify that any authenticating user credentials are correct
• Settings management global component available to all other components in the . application; lookup configuration values given the appropriate configuration key
• User feedback (job and device status, errors, etc.) various events that describe the . current state of the system are generated
• Job function application managers. Each manager is responsible for a specific set of . functionality grouped by a logical theme. For example, the Label Manager handles creating labels based on a set of Label Parameters, the Security Manager handles all issues relating to user management and authentication, and the Print Manager sends commands to and receives responses from the printer hardware

Software Application Description: The software scans user Identification (authentication) and drugs by means of a bar code, and identifies connected devices (printers, computer, and barcode scanner, etc.). The software allows a label to be printed which has a color or colors identifying the type of drug contained in the syringe, the name of the drug or drugs, the units of the drug or drugs, the amount of the drug or drugs, total volume of the syringe, the user ID of the preparer, the time and date of the preparation, and a bar code identifying the contents of the syringe. The software also enables the clinician to document the administration of the drug or drugs by reading the bar code printed on the label and transmitting the drug identification to a third party application (i.e. AIMS system). The system also allows for the labeling of drugs which are combined with other drugs or are diluted. Finally, the software also allows the printing of blank labels, with just the clinician's ID and the data/time.

The major characteristics and functions of the family of devices include:

• Scanning the FDA required drug vial barcode directly from the vial .
• Decoding the manufacturer issued barcode into the required FDA national drug code o (NDC) or Unique Drug Identifier (UDI) number
• Referring the NDC/UDI number to a site managed formulary lookup database .
• Providing audio and ISO-compliant visual "readback" of the drug name .
• Providing a clinical alert if the drug vial is listed as recalled in the site formulary .
• Printing an easy to read, waterproof ISO 26825 compliant color label meeting The . Joint Commission medication management standards and the American Society of Anesthesiologists guidelines for labeling
• Including on same label a printed barcode compliant with national standards for . machine readability allowing integration with an anesthesia information management system (AIMS)
• Providing the basic information by which the printed label barcode can be read to . document medication administration in an AIMS system
• Providing a 2D barcode of information to permit integration with AIMS systems with . potential for alerts if the prepared drug has expired based on preparation time and site specific drug use criteria
• Printing labels with insertion and expiration date and time for IV lines .

The Codonics Safe Labeling System (SLS) is a computer-based bar code scanning and printing system designed for the perioperative environment. It automatically verifies drug identity from NDC (National Drug Code) and other UDI (Unique Drug Identifier) barcodes on drug vials, and then prints compliant labels for prepared drugs and other items used on patients during surgical procedures.

Here's an analysis of the acceptance criteria and study information provided in the document:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present a table of acceptance criteria with corresponding performance metrics in a structured format. Instead, it describes general claims of performance and compliance with relevant standards and regulatory requirements.

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

• Sample Size (Test Set): The document does not specify a distinct numerical sample size for the "test set" in terms of a specific number of drug vials or scenarios tested. It mentions "laboratory (non-clinical environment) and surgical (clinical) tests."
• Data Provenance:
  • Clinical Data: The clinical application of a "prototype Safe Label system at MGH" (Massachusetts General Hospital) is referenced. This suggests a prospective clinical study conducted in the United States.
  • Non-clinical Data: Non-clinical lab tests were conducted utilizing "NDC/UDI codes vials." The provenance of these vials (e.g., specific manufacturers, countries) is not detailed.

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

The document does not provide details on the number of experts or their specific qualifications (e.g., years of experience as a radiologist) used to establish ground truth for the test set. It states that "Medical personnel review the results and inputs processed by the Codonics SLS and offers ample opportunity for competent human intervention." This implies that trained medical professionals, such as physicians, nurses, and technicians (as stated in the "Typical users" section), would effectively act as the ground truth.

4. Adjudication Method for the Test Set

The document does not explicitly describe a formal adjudication method (e.g., 2+1, 3+1) for the test set. The reliance on "Medical personnel review" suggests that clinical judgment and established medical protocols would serve as the adjudication or verification process.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done

No, the document does not indicate that a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was done. It focuses on the standalone performance and compliance of the device rather than comparing human reader performance with and without AI assistance.

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

Yes, a standalone performance assessment was conducted. The document states:

• "Both laboratory (non-clinical environment) and surgical (clinical) tests have shown error free NDC/UDI vial reading and labeling of prepared drugs to ASA/ISO standards."
• "The non-clinical lab tests were conducted utilizing NDC/UDI codes vials scanned and compared to the output of the MGH original SafeLabels system."

This indicates that the device's ability to read barcodes, verify drugs, and print labels was tested without explicit mention of human intervention during the performance measurement itself, although human oversight is always implied in a clinical setting. The "Medical personnel review" is primarily for safety and decision-making, not for correcting algorithm errors during the performance study itself.

7. The Type of Ground Truth Used (expert consensus, pathology, outcomes data, etc.)

The ground truth for the performance evaluations (both non-clinical and clinical) appears to be:

• Known NDC/UDI codes on vials: For the non-clinical lab tests, the device's output was compared to the "output of the MGH original SafeLabels system," which would have had known correct NDC/UDI codes and associated drug information as its reference. This implies that the ground truth was based on the documented content of the drug vials and correct labeling standards.
• ASA/ISO standards: The device's labeling output was verified against these established standards.
• Hospital formulary: The system's ability to correctly identify drugs against a "site managed formulary lookup database" also relies on the accuracy of this database as ground truth.

8. The Sample Size for the Training Set

The document does not explicitly state a sample size for a "training set." This type of device relies on a pre-programmed formulary database rather than a machine learning model that requires a distinct training set in the conventional sense. The "formulary database" effectively serves as the knowledge base for the device.

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

As there isn't a traditional "training set" in the machine learning sense, the "ground truth" for the device's operational knowledge (the formulary) is established through:

• Hospital-managed formulary database: This database contains drug name, concentration, expiration, site-specific warnings, dilutions, and class of drug templates. This formulary is "approved for use by the hospital pharmacy," implying that it is created and maintained by qualified pharmacy professionals using official drug information sources and hospital policies.
• Standards compliance: The system is designed to comply with numerous recognized consensus standards (ASTM, ISO, Joint Commission, ISMP, ASA), which inherently define the correct appearance and content of labels.

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The intended uses of the Horizon XL are identical to the Codonics Horizon® Series Medical Hardcopy Dry Imagers (premarket notification K021054); high resolution hard copy imaging of digital image source material combining color, film, reflective media in one imager. The hardcopy output includes however is not limited to, digital radiography, nuclear medicine, ultrasound, CT, MRI, CR and Radiation Therapy planning. Images are suitable for medical image diagnosis use and referral. The system is intended for use by medical radiologists, imaging modality specialists, and communications to referring physicians. The addition of 14" x 36" and 14" x 51" true size "long" film and (TBD) reflective Direct Vista Paper media permits digital direct orthopaedic application hardcopy including diagnosis and analysis of scoliosis, weight bearing spine/hip/knee, and long bone/hip Prosthetic and orthopedic appliances work-up and surgical planning. Horizon XL is applicable to true-size hardcopy of whole body CT, MRI and Angiographic and Venous flow imaging procedures.

The Horizon® Series Imagers are dry, thermal, grey scale and grev scale/color (Ci model) direct thermal printer/imagers. The devices produce continuous tone, diagnostic quality BM images on transmissive film and reflective incident light viewed media. The color images produced via dye-diffusion technology are photographic medical color matched quality.

The Horizon® XL provides the capability to image True Size Long films used in Orthopedic applications, particularly in spine and long bone studies. These have been traditionally performed utilizing contact-screen film radiography utilizing special long, light-sensitive x-ray films in special long film/screen cassettes. As more imaging departments and centers become committed to PACS implementation and the application of digital radiography (CR and DR) acquisition, Horizon® XL brings long films into the all-digital world.

Here's a breakdown of the acceptance criteria and study information for the Codonics Horizon® XL Medical Long Film Imager, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

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

The document does not explicitly state a specific numerical sample size for "test sets" in the context of clinical studies for a new device. Instead, it refers to:

• Clinical studies, results and outcomes: "Clinical tests have documented effective application and expected results consistent with predicate devices currently in commercial distribution." This implies a comparison to existing data or performance benchmarks, but doesn't detail a separate test set for the XL model itself.
• Data Provenance: Not specified. The document describes a general set of tests performed by the manufacturer (Codonics, Inc. in Middleburg Heights, Ohio, USA).

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

This information is not provided in the document. The filing focuses on the technical and physical characteristics of the printer and its substantial equivalence to a predicate device, rather than detailed human-readability studies for diagnostic accuracy.

4. Adjudication Method for the Test Set

This information is not provided. As detailed above, there isn't a clearly defined "test set" with ground truth established by experts and requiring adjudication.

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

There is no mention of an MRMC comparative effectiveness study, nor any reference to AI assistance. This device is a medical hardcopy printer, not an AI-powered diagnostic tool. The focus is on the quality of printed images.

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

This is not applicable. The device is a printer that outputs images for human interpretation, not an algorithm performing a standalone diagnostic function.

7. The Type of Ground Truth Used

For the clinical tests mentioned ("documented effective application and expected results consistent with predicate devices"), the ground truth would implicitly be the established diagnostic interpretations and clinical utility associated with the images produced by the predicate devices. The document does not specify if pathology or outcomes data was used for direct validation of the XL model against a specific disease.

For the technical criteria (electrical safety, electromagnetic standards, printer resolution), the ground truth is established by the respective industry standards and testing protocols.

8. The Sample Size for the Training Set

There is no mention of a "training set" in the context of machine learning. This device does not employ data-driven machine learning in the conventional sense. Its "training" would be related to engineering design and calibration processes.

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

As above, the concept of a "training set" with established ground truth in a machine learning context is not applicable to this hardcopy imager. The functionality relies on established physics of thermal printing, image processing algorithms (interpolation, scaling), and adherence to industry standards for image display and transmission (DICOM, LUTs).

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The intended uses of the NP-2600 Series Imagers is high resolution hard copy imaging of digital image source material and through the conversion of electronic signals from a wide variety of direct/indirect medical imaging modality outputs. The hardcopy output includes however is not limited to, digital radiography, nuclear medicine, ultrasound, CT, MRI, CR and Radiation Therapy planning; however, does not include digital mammography hardcopy. Images are suitable for medical image diagnosis use and referral. The system is intended for use by medical radiologists, imaging modality specialists, and communications to referring physicians.

The NP-2600 Series Imagers are dry, thermal, color-only (NP-2600), grayscale only (NP-26xx) and grayscale/color(NP-2660) printer/imagers. The devices produce continuous tone, diagnostic quality BM images on transmissive film and reflective incident-light-viewed media. The color images produced via dye-diffusion technology are photographic quality implementing a "CRT to Image" matching process for medical applications.

The provided text describes the Codonics NP-2600 Series Medical Dry Imagers, a device for high-resolution hardcopy imaging of digital medical images. However, it does not contain specific acceptance criteria or details of a study designed to prove the device meets such criteria in terms of analytical or clinical performance metrics.

The document states that "Laboratory tests have documented effective application and expected results consistent with predicate devices currently in commercial distribution and additional verification and validation testing is planned prior to release." This suggests that some testing was performed, but the specifics of acceptance criteria and a detailed study report are not present.

Here's a breakdown of the information that can be extracted and what is missing:

1. Table of Acceptance Criteria and Reported Device Performance

Missing: Specific quantitative acceptance criteria (e.g., minimum spatial frequency response, specific density response tolerances, exact uniformity metrics). The current performance descriptions are generally qualitative or provide a single value without a defined acceptable range.

2. Sample size used for the test set and the data provenance

The document mentions "printer resolution pattern testing" and "clinical studies" were performed. However, it does not specify:

• The sample size used for the test set (e.g., number of images, number of printouts).
• The data provenance (e.g., country of origin of the data, retrospective or prospective).

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

The document mentions "clinical studies" and that images are "suitable for medical image diagnosis use." However, it does not specify:

• The number of experts used to establish ground truth for any test set.
• The qualifications of those experts (e.g., radiologist with 10 years of experience).

4. Adjudication method for the test set

The document does not mention any adjudication method for establishing ground truth or evaluating performance in a test set.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done

The document does not mention a multi-reader multi-case (MRMC) comparative effectiveness study. Therefore, there is no information on the effect size of how much human readers improve with AI vs. without AI assistance (as this is an imaging device, not an AI assistance tool in this context).

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

The device is a hardcopy imager. Its "standalone" performance is described by its physical and performance characteristics (pixel size, resolution, color palate, etc.) and its ability to produce diagnostic quality images. The text states:

• "The NP-2600 Series Imagers are dry, thermal, color-only (NP-2600), grayscale only (NP-26xx) and grayscale/color(NP-2660) printer/imagers."
• "The devices produce continuous tone, diagnostic quality BM images on transmissive film and reflective incident-light-viewed media."
• "The SMPTE resolution and contrast pattern and uniform density response function confirms quality suitable for the intended medical imaging use."

This confirms that its standalone performance in producing images was assessed against relevant industry standards (SMPTE pattern testing). However, specific metrics from these tests beyond general confirmation of suitability are not detailed as specific acceptance criteria or study results.

7. The type of ground truth used

The document implicitly refers to "diagnostic quality" images and "quality suitable for the intended medical imaging use." This suggests that the ground truth for image quality is based on industry standards and expectations for medical image diagnosis, likely involving visual assessment against reference patterns (like the SMPTE resolution and contrast pattern). However, it does not explicitly state the type of ground truth used (e.g., expert consensus, pathology, outcomes data) for a formal study dataset.

8. The sample size for the training set

The device is a printer/imager, not a machine learning algorithm that requires a training set in the typical sense. It relies on "industry standard format conversion software and image rendering algorithms" and "validating digital linear and visual linear routines and verified industry/modality specific Look Up Tables (LUTs)". Therefore, the concept of a "training set" for an algorithm isn't directly applicable in this context.

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

As above, the concept of a training set for an algorithm is not applicable. The "ground truth" for the device's image rendering capabilities would be based on established colorimetry standards, grayscale standards, and visual perception requirements for diagnostic medical imaging.

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The intended uses of the EP-1000 Imagers is high resolution hard copy imaging of digital image source material and through the conversion of electronic signals from a wide variety of direct/indirect medical imaging modality outputs. The hardcopy output includes however is not limited to, nuclear medicine, ultrasound, CT (especially 3-D reconstruction), MRI, and Radiation Therapy planning. Images are suitable for medical image diagnosis use and referral. The system is intended for use by medical radiologists, imaging modality specialists, and communications to referring physicians.

The intended uses are identical to the Codonics NP-1600 Series Medical Printers (Premarket notification K962364) as a color, reflective media imager. The intended uses are identical to the Seiko ColorPoint 1720 (Premarket notification K991282) in terms of color, reflective media output.

The EP-1000 Imagers are dry, thermal, color printer/imagers. The devices produce continuous tone, diagnostic quality B/W and color images on reflective, incident light viewed media. The images produced via dye-diffusion technology are photographic and medical color matched quality.

Digital images are input directly to industry standard parallel (IEEE 1284) or Universal Serial Bus (USB 1.1) interfaces. The EP-1000 recognizes only its own proprietary image format which employs a 24-bit, RGB encoding for each pixel to be printed. Lossy data compression is not employed. Software drivers can be written or adapted for a plurality of host imaging applications and operating systems, which can translate image data of any format to the EP-1000 format.

The EP-1000 does not process or alter the received image data in any way. Host imaging applications or drivers can be designed to pre-compensate image data with gamma, contrast, rotation, scaling, etc; but the EP-1000 can only print the image data as received.

Imaging is accomplished via directly-modulated discrete-element thin-layer linear thermal print head technology. The recording medium is heat activated dye-diffusion of color onto photographic quality ChromaVista paper. The image formation is accomplished without wet chemistry processing common to many laser film imaging systems in use today.

The provided text describes the Codonics EP-1000 Medical Color Dry Imagers, a medical image hardcopy device.

Here's an analysis of the acceptance criteria and study data based on the provided document:

Acceptance Criteria and Reported Device Performance

The document does not explicitly present a table of quantitative acceptance criteria for the EP-1000 Imagers. Instead, acceptance is primarily based on the device demonstrating substantial equivalence to predicate devices in terms of function, intended use, and performance characteristics for medical image hardcopy output.

The document highlights the following performance characteristics as important for medical image hardcopy devices:

• Fidelity of the modulation transfer function
• Spatial frequency response
• Color resolution
• Density response
• Full image field uniformity

The reported performance indicates that the EP-1000 meets these needs:

Summary of Study that Proves the Device Meets Acceptance Criteria:

The study proving the device meets acceptance criteria is primarily an equivalence study, demonstrating that the EP-1000 Imagers are substantially equivalent to already legally marketed predicate devices.

1. Sample size used for the test set and the data provenance:
   The document mentions "printer resolution pattern testing, and clinical studies" but does not specify sample sizes for any test set (e.g., number of images, number of cases). The data provenance (country of origin, retrospective/prospective) is also not mentioned.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
   The document does not specify the number of experts or their qualifications for establishing ground truth related to image quality or diagnostic suitability. It does state that the system is "intended for use by medical radiologists, imaging modality specialists, and communications to referring physicians," implying these are the end-users who would evaluate image suitability.

3. Adjudication method (e.g., 2+1, 3+1, none) for the test set:
   The document does not describe any adjudication method for the test set.

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 study was mentioned. This device is a hardcopy printer, not an AI-assisted diagnostic tool, so such a study would not be applicable in this context.

5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
   This device is a hardware printer. Its "performance" is inherently standalone in the sense that it prints images as it receives them. The document states: "The EP-1000 does not process or alter the received image data in any way." Therefore, its performance is evaluated on the fidelity of the printed output based on the input, rather than an algorithm's diagnostic capabilities. "Laboratory tests have documented expected results consistent with predicate devices currently in commercial distribution, particularly with regard to the predicate NP-1600."

6. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
   For output quality, the ground truth appears to be established by comparison to the "expected results consistent with predicate devices" and confirming "quality suitable for the intended medical imaging use" through resolution patterns (SMPTE) and density response. The document implies that images are "suitable for medical image diagnosis use and referral," suggesting expert judgment on diagnostic quality as an implicit ground truth, though not explicitly detailed.

7. The sample size for the training set:
   This device is a hardware printer, not a machine learning algorithm that requires a training set. Therefore, no training set sample size is applicable or mentioned.

8. How the ground truth for the training set was established:
   As there is no training set for this hardware device, this question is not applicable.

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The intended uses of the Horizon® Series Imagers is high resolution hard copy imaging of digital image source material and through the conversion of electronic signals from a wide variety of direct/indirect medical imaging modality outputs. The hardcopy output includes however is not limited to, digital radiography, nuclear medicine, ultrasound, CT, MRI, CR and Radiation Therapy planning. Images are suitable for medical image diagnosis use and referral. The system is intended for use by medical radiologists, imaging modality specialists, and communications to referring physicians.

The Horizon® Series Imagers are dry, thermal, grey scale (Gs model) and grey scale/color (Ci model) direct thermal printer/imagers. The devices produce continuous tone, diagnostic quality B/W images on transmissive film and reflective incident light viewed media. The color images produced via dye-diffusion technology are photographic medical color matched quality.

This document describes the Codonics Horizon® Series MEDICAL IMAGE HARDCOPY MULTIMEDIA PRINTERS (Horizon® Ci and Gs Medical Multimedia Dry Imagers). These devices are dry, thermal printer/imagers designed to produce continuous tone, diagnostic quality B/W images on transmissive film and reflective media, and color images via dye-diffusion technology. They are intended for high-resolution hard copy imaging from various medical imaging modalities for medical image diagnosis and referral.

Here's an analysis of the acceptance criteria and study information provided:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not present explicit, quantitative acceptance criteria in a table format with corresponding reported performance metrics for diagnostic accuracy (e.g., sensitivity, specificity, AUC). Instead, it describes performance characteristics related to image quality and substantial equivalence to predicate devices.

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

The document mentions "clinical studies" but does not specify the sample size of a test set (e.g., number of images or cases) used for clinical evaluation of diagnostic performance. The provenance of this data (e.g., country of origin, retrospective or prospective) is also not provided. The focus of the reported "clinical studies" appears to be on verifying effective application and consistent results with predicate devices, rather than a diagnostic accuracy study.

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

This information is not provided. The study design described does not involve establishing ground truth for a diagnostic test set in the conventional sense of a clinical diagnostic study for sensitivity/specificity.

4. Adjudication Method for the Test Set

This information is not provided.

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

No MRMC comparative effectiveness study is mentioned. The document primarily focuses on demonstrating substantial equivalence to predicate devices based on technological characteristics and general performance attributes. There is no information about how much human readers improve with or without AI assistance, as this is a medical printer, not an AI-driven diagnostic aid.

6. Standalone (Algorithm Only) Performance Study

As this device is a medical hardcopy printer, the concept of a "standalone (algorithm only)" performance study for diagnostic accuracy is not directly applicable. The "algorithm" here refers to image processing for printing, not a diagnostic algorithm. The performance described (e.g., pixel resolution, grey scale levels) relates to the output quality of the printed image itself.

7. Type of Ground Truth Used

The document does not describe the use of a formal diagnostic ground truth (e.g., pathology, outcomes data, expert consensus) tied to a specific diagnostic performance study. The "clinical studies" mentioned seem to verify the functionality and consistency of the printed output compared to existing devices. The "ground truth" for evaluating the printer's performance refers to physical image quality metrics (e.g., SMPTE pattern resolution, density response, color matching) and consistency with predicate device output.

8. Sample Size for the Training Set

This information is not applicable. The device is a printer that processes images based on established algorithms and look-up tables, not a machine learning algorithm that requires a "training set" in the context of AI/ML models for diagnostic tasks.

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

This information is not applicable.

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The primary intended use of this device is to create hard copy prints or transparencies of medical images acquired from computer networks typically used in a medical environment.

Codonics NP-1600M and NP-1600MD Medical Color Printer produces continuous photographic quality hard copies of electronically created or stored images.

Here's an analysis of the provided text regarding acceptance criteria and the supporting study, structured according to your requested information.

It's important to note that the provided text is a summary statement for a 510(k) submission from 1996 for a medical printer. As such, the concept of "AI" and many of the modern rigorous validation methodologies for AI-powered devices (like deep learning models) were not prevalent or even conceived of in their current form. The document focuses on the printer's performance, not an AI algorithm. Therefore, many of your requested points related to AI/algorithm validation cannot be addressed directly from this text.

Acceptance Criteria and Device Performance for Codonics NP-1600M and NP-1600MD Medical Color Printer

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size: Not specified. The document states "Image quality has been verified by radiologists who compared hard copy images printed on the system compared with the original CRT images." It doesn't mention how many images were used, what types of images, or how many CRT images were compared.
• Data Provenance: Not specified. The source of the "original CRT images" (e.g., country of origin, retrospective/prospective) is not mentioned.

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)

• Number of Experts: Not specified. The document states "radiologists" (plural), indicating at least two.
• Qualifications of Experts: Identified as "radiologists." No specific experience level (e.g., "10 years of experience") or subspecialty is mentioned.

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

• Adjudication Method: Not specified. The document only mentions that radiologists "compared" the images. It does not describe any formal adjudication process for disagreements or consensus building.

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

• MRMC Study: No, an MRMC comparative effectiveness study in the context of AI assistance was not done. This document predates the widespread concept and rigorous evaluation of AI in medical imaging. The "effectiveness" here refers to the printer's ability to produce faithful hard copies, not the improvement of human readers with AI assistance.

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

• Standalone Study: No, this device is a printer, which is a hardware output device. There is no "algorithm" in the sense of a diagnostic AI model that would have standalone performance. The "effectiveness validation" was about the printer's output quality, not an algorithm's diagnostic accuracy.

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

• Type of Ground Truth: The ground truth for image quality assessment was the original CRT images themselves, as judged by radiologists. The radiologists were comparing the hard copy print to the original digital display (CRT image) to ensure fidelity. This is a form of expert visual comparison to a direct reference, rather than a diagnostic ground truth like pathology.

8. The sample size for the training set

• Training Set Sample Size: Not applicable. This document describes a medical printer, not a machine learning model. Therefore, there is no "training set" in the context of AI. The "extensive testing" mentioned was for hardware and software functionality and user experience, not for training an algorithm.

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

• Ground Truth for Training Set: Not applicable, as there is no training set for an AI model.

Summary of Limitations given the document type:

This 1996 510(k) summary for a medical printer is a product of its time and regulatory context. It addresses parameters relevant to a physical device (safety certifications, fidelity of output) rather than the rigorous statistical and clinical validation studies required for contemporary AI-powered diagnostic devices. Therefore, many of the questions asked cannot be answered or are not applicable based on the provided text.

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