(64 days)
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No
The summary provides no information suggesting the use of AI or ML. The device description is not available, and there are no mentions of AI, ML, image processing, or data sets typically associated with AI/ML development.
No
The device is described as being used to "collect and remove tissue," which indicates a surgical tool rather than a device intended for therapy or treatment.
No
Explanation: The device description states its purpose is to "collect and remove tissue during laparoscopic procedures," which describes a surgical tool for treatment, not a device for diagnosing medical conditions.
No
The intended use clearly describes a physical device ("collect and remove tissue during laparoscopic procedures") and there is no mention of software as the primary or sole component.
Based on the provided information, the CORE Pouch is not an IVD (In Vitro Diagnostic).
Here's why:
- Intended Use: The intended use is to "collect and remove tissue during laparoscopic procedures." This describes a surgical tool used in vivo (within the body) during a procedure, not a device used in vitro (outside the body) to examine specimens for diagnostic purposes.
- Lack of IVD Characteristics: The description does not mention any of the typical characteristics of an IVD, such as:
- Analyzing biological samples (blood, urine, tissue after removal for analysis).
- Providing diagnostic information.
- Using reagents or assays.
The CORE Pouch appears to be a surgical accessory used for tissue management during a procedure.
N/A
Intended Use / Indications for Use
The CORE Pouch is used when the surgeon desires to collect and remove tissue during laparoscopic procedures.
Product codes
GCJ
Device Description
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Mentions image processing
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Mentions AI, DNN, or ML
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Input Imaging Modality
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Anatomical Site
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Indicated Patient Age Range
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Intended User / Care Setting
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Description of the training set, sample size, data source, and annotation protocol
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Description of the test set, sample size, data source, and annotation protocol
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Summary of Performance Studies
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Key Metrics
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Predicate Device(s)
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Reference Device(s)
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Predetermined Change Control Plan (PCCP) - All Relevant Information
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§ 876.1500 Endoscope and accessories.
(a)
Identification. An endoscope and accessories is a device used to provide access, illumination, and allow observation or manipulation of body cavities, hollow organs, and canals. The device consists of various rigid or flexible instruments that are inserted into body spaces and may include an optical system for conveying an image to the user's eye and their accessories may assist in gaining access or increase the versatility and augment the capabilities of the devices. Examples of devices that are within this generic type of device include cleaning accessories for endoscopes, photographic accessories for endoscopes, nonpowered anoscopes, binolcular attachments for endoscopes, pocket battery boxes, flexible or rigid choledochoscopes, colonoscopes, diagnostic cystoscopes, cystourethroscopes, enteroscopes, esophagogastroduodenoscopes, rigid esophagoscopes, fiberoptic illuminators for endoscopes, incandescent endoscope lamps, biliary pancreatoscopes, proctoscopes, resectoscopes, nephroscopes, sigmoidoscopes, ureteroscopes, urethroscopes, endomagnetic retrievers, cytology brushes for endoscopes, and lubricating jelly for transurethral surgical instruments. This section does not apply to endoscopes that have specialized uses in other medical specialty areas and that are covered by classification regulations in other parts of the device classification regulations.(b)
Classification —(1)Class II (special controls). The device, when it is an endoscope disinfectant basin, which consists solely of a container that holds disinfectant and endoscopes and accessories; an endoscopic magnetic retriever intended for single use; sterile scissors for cystoscope intended for single use; a disposable, non-powered endoscopic grasping/cutting instrument intended for single use; a diagnostic incandescent light source; a fiberoptic photographic light source; a routine fiberoptic light source; an endoscopic sponge carrier; a xenon arc endoscope light source; an endoscope transformer; an LED light source; or a gastroenterology-urology endoscopic guidewire, is exempt from the premarket notification procedures in subpart E of part 807 of this chapter subject to the limitations in § 876.9.(2) Class I for the photographic accessories for endoscope, miscellaneous bulb adapter for endoscope, binocular attachment for endoscope, eyepiece attachment for prescription lens, teaching attachment, inflation bulb, measuring device for panendoscope, photographic equipment for physiologic function monitor, special lens instrument for endoscope, smoke removal tube, rechargeable battery box, pocket battery box, bite block for endoscope, and cleaning brush for endoscope. The devices subject to this paragraph (b)(2) are exempt from the premarket notification procedures in subpart E of part 807of this chapter, subject to the limitations in § 876.9.
0
Image /page/0/Picture/1 description: The image shows the logo for the U.S. Department of Health & Human Services. The logo features a stylized depiction of an eagle or bird-like figure with three curved lines representing its body and wings. The text "DEPARTMENT OF HEALTH & HUMAN SERVICES - USA" is arranged in a circular fashion around the bird symbol.
Food and Drug Administration 9200 Corporate Boulevard Rockville MD 20850
SEP - 1 1999
Mr. Thomas M. McIntosh Director, Ouality Assurance and Regulatory Affairs Core Dynamics, Inc. 11222 St. John's Industrial Parkway Jacksonville, Florida 32246
K992190 Re: Trade Name: Core Pouch Regulatory Class: II Product Code: GCJ Dated: June 25, 1999 Received: June 29, 1999
Dear Mr. McIntosh:
We have reviewed your Section 510(k) notification of intent to market the device referenced above and we have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to 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). You may, therefore, market the device, subject to the general controls provisions of the Act. 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.
If your device is classified (see above) into either class II (Special Controls) or class III (Premarket Approval), it may be subject to such additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 895. A substantially equivalent determination assumes compliance with the current Good Manufacturing Practice requirement, as set forth in the Quality System Regulation (QS) for Medical Devices: General regulation (21 CFR Part 820) and that, through periodic (QS) inspections, the Food and Drug Administration (FDA) will verify such assumptions. Failure to comply with the GMP regulation may result in regulatory action. In addition, FDA may publish further announcements concerning your device in the Federal Register. Please note: this response to your premarket notification submission does not affect any obligation you might have under sections 531 through 542 of the Act for devices under the Electronic Product Radiation Control provisions, or other Federal laws or regulations.
1
Page 2 -- Mr. Thomas M. McIntosh
This letter will allow you to begin marketing your device as described in your 510(k) premarket notification. The FDA finding of substantial equivalence of your device to a legally marketed predicate device results in a classification for your device and thus, permits your device to proceed to the market.
If you desire specific advice for your device on our labeling regulation (21 CFR Part 801 and additionally 809.10 for in vitro diagnostic devices), please contact the Office of Compliance at (301) 594-4595. Additionally, for questions on the promotion and advertising of your device, please contact the Office of Compliance at (301) 594-4639. Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 807.97). Other general information on your responsibilities under the Act may be obtained from the Division of Small Manufacturers Assistance at its toll-free number (800) 638-2041 or (301) 443-6597 or at its internet address "http://www.fda.gov/cdrh/dsmamain.html".
Sincerely yours,
\section{Introduction} 1.1 Background and Motivation: The field of computer vision has witnessed remarkable advancements in recent years, particularly in tasks such as image classification, object detection, and image segmentation. These advancements have been largely driven by the development of deep learning models, which have demonstrated superior performance compared to traditional methods. However, the success of deep learning models heavily relies on the availability of large-scale labeled datasets, which are often expensive and time-consuming to acquire. In many real-world scenarios, obtaining sufficient labeled data is a significant challenge, limiting the applicability of deep learning models. To address this challenge, researchers have explored various techniques for training models with limited labeled data, such as transfer learning, semi-supervised learning, and active learning. Transfer learning involves leveraging knowledge gained from pre-trained models on large datasets to improve the performance of models trained on smaller datasets. Semi-supervised learning utilizes both labeled and unlabeled data to train models, while active learning aims to select the most informative samples for labeling, thereby reducing the labeling effort required. 1.2 Problem Statement: Despite the progress made in training models with limited labeled data, there remains a need for more effective and efficient techniques that can further improve the performance of computer vision models in low-data regimes. In particular, there is a lack of methods that can effectively leverage the complementary strengths of different learning paradigms, such as transfer learning and semi-supervised learning. Furthermore, the selection of appropriate pre-trained models and the design of effective semi-supervised learning strategies are critical factors that can significantly impact the performance of models trained with limited labeled data. 1.3 Objectives: The main objectives of this research are: 1. To investigate the effectiveness of combining transfer learning and semi-supervised learning for training computer vision models with limited labeled data. 2. To develop a novel approach for selecting appropriate pre-trained models for transfer learning based on the characteristics of the target dataset. 3. To design an effective semi-supervised learning strategy that can leverage both labeled and unlabeled data to improve the performance of computer vision models. 4. To evaluate the performance of the proposed approach on various benchmark datasets and compare it with state-of-the-art methods. 1.4 Scope: This research will focus on the following aspects: 1. Image classification tasks. 2. Deep learning models, specifically convolutional neural networks (CNNs). 3. Transfer learning techniques using pre-trained models on ImageNet. 4. Semi-supervised learning strategies such as self-training and consistency regularization. 5. Evaluation on benchmark datasets such as CIFAR-10, CIFAR-100, and SVHN. 1.5 Contributions: The main contributions of this research are: 1. A novel approach for combining transfer learning and semi-supervised learning for training computer vision models with limited labeled data. 2. A method for selecting appropriate pre-trained models for transfer learning based on the characteristics of the target dataset. 3. An effective semi-supervised learning strategy that can leverage both labeled and unlabeled data to improve the performance of computer vision models. 4. Experimental results demonstrating the effectiveness of the proposed approach on various benchmark datasets. 1.6 Organization of the Thesis: The thesis is organized as follows: 1. Chapter 2 provides a review of the related work in transfer learning, semi-supervised learning, and active learning. 2. Chapter 3 presents the proposed approach for combining transfer learning and semi-supervised learning. 3. Chapter 4 describes the experimental setup and evaluation metrics used in this research. 4. Chapter 5 presents the experimental results and compares the performance of the proposed approach with state-of-the-art methods. 5. Chapter 6 concludes the thesis and discusses future research directions.
Celia M. Witten, Ph.D., M.D. Director Division of General and Restorative Devices Office of Device Evaluation Center for Devices and Radiological Health
Enclosure
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Page 1 of 1
510(k) NUMBER (IF KNOWN):
DEVICE NAME:
-- ' :
CORE POUCH
INDICATIONS FOR USE:
The CORE Pouch is used when the surgeon desires to collect and remove tissue during laparoscopic procedures.
(Division Sigh-Off)
Division of General Restorative Devices
510(k) Number. , Ka92197
(PLEASE DO NOT WRITE BELOW THIS LINE - CONTINUE ON ANOTHER PAGE IF NEEDED.)
Concurrence of CDRH, Office of Device Evaluation (ODE)
Prescription Use 5 (Per 21 CFR 801.109)
バ
OR
Over-The Counter Use (Optional Format)