Objective
To present the International Parkinson and Movement Disorder Society (MDS) Clinical Diagnostic Criteria for Parkinson9s disease.
Background
Although several diagnostic criteria for Parkinson9s disease have been proposed, none have been officially adopted by an official Parkinson society. Moreover, the commonest-used criteria, the UK brain bank, were created more than 25 years ago. In recognition of the lack of standard criteria, the MDS initiated a task force to design new diagnostic criteria for clinical Parkinson9s disease.
Methods/Results
The MDS-PD Criteria are intended for use in clinical research, but may also be used to guide clinical diagnosis. The benchmark is expert clinical diagnosis; the criteria aim to systematize the diagnostic process, to make it reproducible across centers and applicable by clinicians with less expertise. Although motor abnormalities remain central, there is increasing recognition of non-motor manifestations; these are incorporated into both the current criteria and particularly into separate criteria for prodromal PD. Similar to previous criteria, the MDS-PD Criteria retain motor parkinsonism as the core disease feature, defined as bradykinesia plus rest tremor and/or rigidity. Explicit instructions for defining these cardinal features are included. After documentation of parkinsonism, determination of PD as the cause of parkinsonism relies upon three categories of diagnostic features; absolute exclusion criteria (which rule out PD), red flags (which must be counterbalanced by additional supportive criteria to allow diagnosis of PD), and supportive criteria (positive features that increase confidence of PD diagnosis). Two levels of certainty are delineated: Clinically-established PD (maximizing specificity at the expense of reduced sensitivity), and Probable PD (which balances sensitivity and specificity).
Conclusion
The MDS criteria retain elements proven valuable in previous criteria and omit aspects that are no longer justified, thereby encapsulating diagnosis according to current knowledge. As understanding of PD expands, criteria will need continuous revision to accommodate these advances. Disclosure: Dr. Postuma has received personal compensation for activities with Roche Diagnostics Corporation and Biotie Therapies. Dr. Berg has received research support from Michael J. Fox Foundation, the Bundesministerium fur Bildung und Forschung (BMBF), the German Parkinson Association and Novartis GmbH.

MDS Clinical Diagnostic Criteria for Parkinson's Disease (S19.001)

The science of solving clinical problems by analyzing images generated in clinical practice is known as medical image analysis. The aim is to extract information in an affective and efficient manner for improved clinical diagnosis. The recent advances in the field of biomedical engineering have made medical image analysis one of the top research and development area. One of the reasons for this advancement is the application of machine learning techniques for the analysis of medical images. Deep learning is successfully used as a tool for machine learning, where a neural network is capable of automatically learning features. This is in contrast to those methods where traditionally hand crafted features are used. The selection and calculation of these features is a challenging task. Among deep learning techniques, deep convolutional networks are actively used for the purpose of medical image analysis. This includes application areas such as segmentation, abnormality detection, disease classification, computer aided diagnosis and retrieval. In this study, a comprehensive review of the current state-of-the-art in medical image analysis using deep convolutional networks is presented. The challenges and potential of these techniques are also highlighted.

Medical Image Analysis using Convolutional Neural Networks: A Review

Medical image retrieval using deep convolutional neural network

The science of solving clinical problems by analyzing images generated in clinical practice is known as medical image analysis. The aim is to extract information in an effective and efficient manner for improved clinical diagnosis. The recent advances in the field of biomedical engineering has made medical image analysis one of the top research and development area. One of the reason for this advancement is the application of machine learning techniques for the analysis of medical images. Deep learning is successfully used as a tool for machine learning, where a neural network is capable of automatically learning features. This is in contrast to those methods where traditionally hand crafted features are used. The selection and calculation of these features is a challenging task. Among deep learning techniques, deep convolutional networks are actively used for the purpose of medical image analysis. This include application areas such as segmentation, abnormality detection, disease classification, computer aided diagnosis and retrieval. In this study, a comprehensive review of the current state-of-the-art in medical image analysis using deep convolutional networks is presented. The challenges and potential of these techniques are also highlighted.

https://hesso.tind.io/record/2362/files/Muller_2018_large_scale_retrieval.pdf

Large-scale retrieval for medical image analytics: A comprehensive review.

With the widespread dissemination of picture archiving and communication systems (PACSs) in hospitals, the amount of imaging data is rapidly increasing Effective image retrieval systems are required to manage these complex and large image databases The authors reviewed the past development and the present state of medical image retrieval systems including text-based and content-based systems In order to provide a more effective image retrieval service, the intelligent content-based retrieval systems combined with semantic systems are required

/pdf/medical-image-retrieval-past-and-present-45hrha8wlx.pdf

Medical image retrieval: past and present

Purpose
The aim of this study was to investigate the relationship between the maximum standardized uptake values (SUVmax) of primary renal cancers with and without metastatic lesions, if any. We also studied the relationship between the size of primary renal cancers and their SUVmax, and tried to find a clinical value of 18F-FDG PET-CT for the initial evaluation of renal cell carcinoma (RCC).

/pdf/can-initial-18-f-fdg-pet-ct-imaging-give-information-on-1tcvgqobh1.pdf

Can Initial 18 F-FDG PET-CT Imaging Give Information on Metastasis in Patients with Primary Renal Cell Carcinoma?

Purpose
To assess the usefulness of 18F-fluorodeoxyglucose PET/CT in the detection of bone marrow (BM) involvement of high-grade non-Hodgkin’s lymphoma (NHL).

/pdf/usefulness-of-18-f-fdg-pet-ct-for-the-evaluation-of-bone-4tvov0dn06.pdf

Usefulness of 18 F-FDG PET/CT for the Evaluation of Bone Marrow Involvement in Patients with High-Grade Non-Hodgkin’s Lymphoma

Initial diagnostic workup of parkinsonism: Dopamine transporter positron emission tomography versus susceptibility map-weighted imaging at 3T.

As the use of positron emission tomography-computed tomography (PET-CT) has increased rapidly, there is a need to retrieve relevant medical images that can assist image interpretation However, the images themselves lack the explicit information needed for query We constructed a semantically structured database of nuclear medicine images using the Annotation and Image Markup (AIM) format and evaluated the ability the AIM annotations to improve image search We created AIM annotation templates specific to the nuclear medicine domain and used them to annotate 100 nuclear medicine PET-CT studies in AIM format using controlled vocabulary We evaluated image retrieval from 20 specific clinical queries As the gold standard, two nuclear medicine physicians manually retrieved the relevant images from the image database using free text search of radiology reports for the same queries We compared query results with the manually retrieved results obtained by the physicians The query performance indicated a 98 % recall for simple queries and a 89 % recall for complex queries In total, the queries provided 95 % (75 of 79 images) recall, 100 % precision, and an F1 score of 097 for the 20 clinical queries Three of the four images missed by the queries required reasoning for successful retrieval Nuclear medicine images augmented using semantic annotations in AIM enabled high recall and precision for simple queries, helping physicians to retrieve the relevant images Further study using a larger data set and the implementation of an inference engine may improve query results for more complex queries

/pdf/building-and-querying-rdf-owl-database-of-semantically-3ayprl4txl.pdf

Haejun Lee

Papers

Medical image retrieval: past and present

Can Initial 18 F-FDG PET-CT Imaging Give Information on Metastasis in Patients with Primary Renal Cell Carcinoma?

Usefulness of 18 F-FDG PET/CT for the Evaluation of Bone Marrow Involvement in Patients with High-Grade Non-Hodgkin’s Lymphoma

Initial diagnostic workup of parkinsonism: Dopamine transporter positron emission tomography versus susceptibility map-weighted imaging at 3T.

Building and Querying RDF/OWL Database of Semantically Annotated Nuclear Medicine Images.