scholarly journals Bias Investigation in Artificial Intelligence Systems for Early Detection of Parkinson’s Disease: A Narrative Review

Diagnostics ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 166
Author(s):  
Sudip Paul ◽  
Maheshrao Maindarkar ◽  
Sanjay Saxena ◽  
Luca Saba ◽  
Monika Turk ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Big Data ◽  
Clinical Evaluation ◽  
Clinical Signs ◽  
Good Prognosis ◽  
Medical Attention ◽  
Data Framework ◽  
High Bias

Background and Motivation: Diagnosis of Parkinson’s disease (PD) is often based on medical attention and clinical signs. It is subjective and does not have a good prognosis. Artificial Intelligence (AI) has played a promising role in the diagnosis of PD. However, it introduces bias due to lack of sample size, poor validation, clinical evaluation, and lack of big data configuration. The purpose of this study is to compute the risk of bias (RoB) automatically. Method: The PRISMA search strategy was adopted to select the best 39 AI studies out of 85 PD studies closely associated with early diagnosis PD. The studies were used to compute 30 AI attributes (based on 6 AI clusters), using AP(ai)Bias 1.0 (AtheroPointTM, Roseville, CA, USA), and the mean aggregate score was computed. The studies were ranked and two cutoffs (Moderate-Low (ML) and High-Moderate (MH)) were determined to segregate the studies into three bins: low-, moderate-, and high-bias. Result: The ML and HM cutoffs were 3.50 and 2.33, respectively, which constituted 7, 13, and 6 for low-, moderate-, and high-bias studies. The best and worst architectures were “deep learning with sketches as outcomes” and “machine learning with Electroencephalography,” respectively. We recommend (i) the usage of power analysis in big data framework, (ii) that it must undergo scientific validation using unseen AI models, and (iii) that it should be taken towards clinical evaluation for reliability and stability tests. Conclusion: The AI is a vital component for the diagnosis of early PD and the recommendations must be followed to lower the RoB.

scholarly journals Will Artificial Intelligence Replace the Movement Disorders Specialist for Diagnosing and Managing Parkinson’s Disease?

2021 ◽  
pp. 1-6
Author(s):  
Matt Landers ◽  
Suchi Saria ◽  
Alberto J. Espay
Keyword(s):  
Artificial Intelligence ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Precision Medicine ◽  
Movement Disorders ◽  
Living Environment ◽  
Patient Status ◽  
Patient Symptom ◽  
Molecular Therapies ◽  
Improve Patient

The use of artificial intelligence (AI) to help diagnose and manage disease is of increasing interest to researchers and clinicians. Volumes of health data are generated from smartphones and ubiquitous inexpensive sensors. By using these data, AI can offer otherwise unobtainable insights about disease burden and patient status in a free-living environment. Moreover, from clinical datasets AI can improve patient symptom monitoring and global epidemiologic efforts. While these applications are exciting, it is necessary to examine both the utility and limitations of these novel analytic methods. The most promising uses of AI remain aspirational. For example, defining the molecular subtypes of Parkinson’s disease will be assisted by future applications of AI to relevant datasets. This will allow clinicians to match patients to molecular therapies and will thus help launch precision medicine. Until AI proves its potential in pushing the frontier of precision medicine, its utility will primarily remain in individualized monitoring, complementing but not replacing movement disorders specialists.

Identifying drugs with disease‐modifying potential in Parkinson's disease using artificial intelligence and pharmacoepidemiology

2020 ◽  
Vol 29 (8) ◽  
pp. 864-872
Author(s):  
Laura C. Maclagan ◽  
Naomi P. Visanji ◽  
Yi Cheng ◽  
Mina Tadrous ◽  
Alix M. B. Lacoste ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Disease Modifying

scholarly journals The universal non-neuronal nature of Parkinson's disease: A theory

2015 ◽  
Author(s):  
André Valente ◽  
Altynay Adilbayeva ◽  
Tursonjan Tokay ◽  
Albert Rizvanov
Keyword(s):  
Gene Expression ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Mitochondrial Dna ◽  
Late Onset ◽  
Clinical Signs ◽  
Global Gene Expression ◽  
Neuron Death ◽  
Hematopoietic Stem ◽  
Hematopoietic Stem Cell Niche

Various recent developments of relevance to Parkinson's disease (PD) are discussed and integrated into a comprehensive hypothesis on the nature, origin and inter-cellular mode of propagation of late-onset sporadic PD. We propose to define sporadic PD as a characteristic pathological deviation in the global gene expression program of a cell: the PD expression-state, or PD-state for short. Although a universal cell-generic state, the PD-state deviation would be particularly damaging in a neuronal context, ultimately leading to neuron death and the ensuing observed clinical signs. We review why age accumulated damage caused by oxidative stress in mitochondria could be the trigger for a primordial cell to shift to the PD-state. We put forward hematopoietic cells could be the first to acquire the PD-state, at hematopoiesis, from the disruption in reactive oxygen species (ROS) homeostasis that arises with age in the hematopoietic stem-cell niche. We argue why, nonetheless, such a process is unlikely to explain the shift to the PD-state of all the subsequently affected cells in a patient, thus indicating the existence of a distinct mechanism of propagation of the PD-state. We highlight recent findings on the intercellular exchange of mitochondrial DNA and the ability of mitochondrial DNA to modulate the cellular global gene expression state and propose this could form the basis for the intercellular propagation of the PD-state.

scholarly journals Big Data and Parkinson’s Disease: Exploration, Analyses, and Data Challenges.

2018 ◽  
Author(s):  
Mahalakshmi SenthilarumugamVeilukandammal ◽  
Sree Nilakanta ◽  
Baskar Ganapathysubramanian ◽  
Vellareddy Anantharam ◽  
Anumantha Kanthasamy ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Big Data

scholarly journals WHAT’S OLD IS NEW: USING ARTIFICIAL INTELLIGENCE TO ACCELERATE DISCOVERY OF NEW TREATMENTS

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S16-S17
Author(s):  
Connie Marras ◽  
Laura C Maclagan ◽  
Yi Cheng ◽  
Naomi Visanji ◽  
Mina Tadrous ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Conditional Logistic Regression ◽  
Disease Diagnosis ◽  
Success Rates ◽  
Disease Modifying Drugs ◽  
Logistic Regression Models ◽  
Intelligence System ◽  
Disease Modifying

Abstract Given the high cost of drug development and low success rates, repurposing drugs already proven safe provides a promising avenue for identifying effective therapies with additional indications. The IBM Watson artificial intelligence system was used to search 1.3 million Medline abstracts to prioritize medications that may be potentially disease-modifying in Parkinson’s disease. We assessed patterns of use of the top 50 Watson-ranked drugs among 14,866 adults with Parkinson’s disease aged 70 and older who were matched to persons without Parkinson’s disease on age, sex, and comorbidity. Sociodemographic characteristics, chronic conditions, and use of other medications were compared using standardized differences. Patterns of potentially disease-modifying drug use were examined prior to and following ascertainment of Parkinson’s disease. Preliminary findings from multivariable conditional logistic regression models on the association between previous exposure to potentially disease-modifying drugs and Parkinson’s disease diagnosis will be presented.

Clinical evaluation of the periodontal health condition and oral health awareness in Parkinson's disease patients

Gerodontology ◽  
2013 ◽  
Vol 32 (2) ◽  
pp. 100-106 ◽  
Author(s):  
Avani R. Pradeep ◽  
Sonender P. Singh ◽  
Santosh S. Martande ◽  
Arjun P. Raju ◽  
Tagya Rustagi ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Oral Health ◽  
Clinical Evaluation ◽  
Health Condition ◽  
Health Awareness ◽  
Periodontal Health

scholarly journals Magnetic Resonance Spectroscopy in the Diagnosis of Dementia with Lewy Bodies

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Radoslaw Magierski ◽  
Tomasz Sobow
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Magnetic Resonance ◽  
Magnetic Resonance Spectroscopy ◽  
Dementia With Lewy Bodies ◽  
Clinical Symptoms ◽  
Clinical Signs ◽  
Lewy Bodies ◽  
Resonance Spectroscopy ◽  
Diagnosis Of Dementia

Dementia with Lewy bodies (DLB) is considered to be the second most frequent primary degenerative dementing illness after Alzheimer’s disease (AD). DLB, together with Parkinson’s disease (PD), Parkinson’s disease with dementia (PDD) belong toα-synucleinopathies—a group of neurodegenerative diseases associated with pathological accumulation of theα-synuclein protein. Dementia due to PD and DLB shares clinical symptoms and neuropsychological profiles. Moreover, the core features and additional clinical signs and symptoms for these two very similar diseases are largely the same. Neuroimaging seems to be a promising method in differential diagnosis of dementia studies. The development of imaging methods or other objective measures to supplement clinical criteria for DLB is needed and a method which would accurately facilitate diagnosis of DLB prior to death is still being searched. Proton magnetic resonance spectroscopy (1H-MRS) provides a noninvasive method of assessing anin vivobiochemistry of brain tissue. This review summarizes the main results obtained from the application of neuroimaging techniques in DLB cases focusing on1H-MRS.

scholarly journals Autologous Adrenal Medullary, Fetal Mesencephalic, and Fetal Adrenal Brain Transplantation in Parkinson's Disease: A Long-Term Postoperative Follow-Up

1991 ◽  
Vol 2 (3-4) ◽  
pp. 157-164 ◽  
Author(s):  
Ignacio Madrazo ◽  
Rebecca Franco-Bourland ◽  
Maricarmen Aguilera ◽  
Feggy Ostrosky-Solis ◽  
Mario Madrazo ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Clinical Evaluation ◽  
Clinical Status ◽  
Substantial Improvement ◽  
Neuropsychological Changes ◽  
Postural Imbalance ◽  
Tissue Grafts ◽  
Gait Disturbances

We report on the clinical status of 5 patients with Parkinson's disease (PD) 3 years after autologous adrenal medullary (AM)-to-caudate nucleus (CN) implanfion, and of 2 PD patients, 2 years after fetal ventral mesencephalon (VM)- and fetal adrenal (A)-to-CN homotransplantation. Current clinical evaluation of 4 of the AM grafted patients revealed sustained bilateral amelioration of their PD signs, most notably of rgidity, postural imbalance and gait disturbances, resulting in a substantial improvement in their quality of life. the disease-related dystonia of one of them disappeared only 2 years after surgery. The levodopa requirements of 2 of these patients and the anticholinergic therapy of another have been reduced. In agreement with the satisfactory clinical evaluation of these 4 patients, their neuropsychological and electrophysiological improvements, initially registered 3 months after surgery, have been maintained for 3 years. After 1 year of significant recovery, the 5th patient of this group has almost returned to her preoperative state.The 2 homotransplanted patients also showed sustained bilateral improvement of their PD signs. Two years after surgery, the most improved signs of the fetal VM case were rigidity, bradykinesia, postural imbalance, gait disturbances and facial expression. The fetal A case has only shown amelioration of rigidity and bradykinesia. Neither of them has shown significant neuropsychological changes. Their current levodopa requirements are less than before surgery.The improvements shown here by PD patients after brain tissue grafts go beyond those obtained using any other therapeutic approach, when levodopa fails. Although more studies and the development of these procedures are obviously required, these initial human trials appear to be resisting the test of time.

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