Artificial Intelligence in Medicine: Chances and Challenges for Wide Clinical Adoption

Varghese, Julian

doi:10.1159/000511930

Cited by 59 publications

(53 citation statements)

References 43 publications

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“…Despite the high prediction accuracy obtained by many deep learning models proposed in various automated PD detection studies, the adoption of the deep learning model as a CAD tool in healthcare is currently not supported [21,22]. In their current form, neither neurologists nor other healthcare workers are comfortable to rely on CAD tools to diagnose the PD.…”

Section: Challenges Faced By Cad Tools In Healthcare Adoptionmentioning

confidence: 99%

“…Also, the selection of the most relevant features must be carried out by an experienced expert system that is knowledgeable in terms of various feature selection tools [15,16]. This has led to the somewhat poor adoption of machine learning models as the future CAD tools as feature extraction and selection can be complicated procedures comprehensible by machine learning experts, but not so by the end-user of the CAD tool [21,22]. Such end-users may involve healthcare experts such as practicing clinicians, health researchers, or other domain applications.…”

Section: Introductionmentioning

confidence: 99%

“…Deep learning models are also known as the 'black box' so it is almost impossible to clearly understand the mechanisms behind a deep learning model when it makes a given prediction [22,23]. Despite achieving high prediction accuracy, end-users of the CAD tools (e.g., neurologists and healthcare workers) cannot make a diagnosis without sufficient evidence, and this evidence is not currently provided by deep learning models [21,23].…”

mentioning

confidence: 99%

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Application of Deep Learning Models for Automated Identification of Parkinson’s Disease: A Review (2011–2021)

Loh

Hong²,

Ooi

et al. 2021

Sensors

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Parkinson’s disease (PD) is the second most common neurodegenerative disorder affecting over 6 million people globally. Although there are symptomatic treatments that can increase the survivability of the disease, there are no curative treatments. The prevalence of PD and disability-adjusted life years continue to increase steadily, leading to a growing burden on patients, their families, society and the economy. Dopaminergic medications can significantly slow down the progression of PD when applied during the early stages. However, these treatments often become less effective with the disease progression. Early diagnosis of PD is crucial for immediate interventions so that the patients can remain self-sufficient for the longest period of time possible. Unfortunately, diagnoses are often late, due to factors such as a global shortage of neurologists skilled in early PD diagnosis. Computer-aided diagnostic (CAD) tools, based on artificial intelligence methods, that can perform automated diagnosis of PD, are gaining attention from healthcare services. In this review, we have identified 63 studies published between January 2011 and July 2021, that proposed deep learning models for an automated diagnosis of PD, using various types of modalities like brain analysis (SPECT, PET, MRI and EEG), and motion symptoms (gait, handwriting, speech and EMG). From these studies, we identify the best performing deep learning model reported for each modality and highlight the current limitations that are hindering the adoption of such CAD tools in healthcare. Finally, we propose new directions to further the studies on deep learning in the automated detection of PD, in the hopes of improving the utility, applicability and impact of such tools to improve early detection of PD globally.

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Section: Challenges Faced By Cad Tools In Healthcare Adoptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Application of Deep Learning Models for Automated Identification of Parkinson’s Disease: A Review (2011–2021)

Loh

Hong²,

Ooi

et al. 2021

Sensors

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“…Moreover, our work contributes to new digital and objective biomarkers, which have the potential for disease stratification or disease monitoring of PD patients to provide personalized care and treatment optimization. As for all clinical decision support, further quality and risk management and medical device approval is necessary for integration into routine diagnostics [ 39 ]. To the best of our knowledge, our study generated the largest set of smartwatch-based measurements in a neurological examination with structured clinical data on symptoms and medical history.…”

Section: Discussionmentioning

confidence: 99%

Sensor Validation and Diagnostic Potential of Smartwatches in Movement Disorders

Varghese

Alen

Fujarski

et al. 2021

Sensors

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Smartwatches provide technology-based assessments in Parkinson’s Disease (PD). It is necessary to evaluate their reliability and accuracy in order to include those devices in an assessment. We present unique results for sensor validation and disease classification via machine learning (ML). A comparison setup was designed with two different series of Apple smartwatches, one Nanometrics seismometer and a high-precision shaker to measure tremor-like amplitudes and frequencies. Clinical smartwatch measurements were acquired from a prospective study including 450 participants with PD, differential diagnoses (DD) and healthy participants. All participants wore two smartwatches throughout a 15-min examination. Symptoms and medical history were captured on the paired smartphone. The amplitude error of both smartwatches reaches up to 0.005 g, and for the measured frequencies, up to 0.01 Hz. A broad range of different ML classifiers were cross-validated. The most advanced task of distinguishing PD vs. DD was evaluated with 74.1% balanced accuracy, 86.5% precision and 90.5% recall by Multilayer Perceptrons. Deep-learning architectures significantly underperformed in all classification tasks. Smartwatches are capable of capturing subtle tremor signs with low noise. Amplitude and frequency differences between smartwatches and the seismometer were under the level of clinical significance. This study provided the largest PD sample size of two-hand smartwatch measurements and our preliminary ML-evaluation shows that such a system provides powerful means for diagnosis classification and new digital biomarkers, but it remains challenging for distinguishing similar disorders.

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“…Examples appear in this edition of Visceral Medicine , in the paper by Jell et al [8] on the analysis of long-term manometry, and that by Varghese [9] on the analysis of electronic health records. Decision-making support systems (e.g., in oncology) are highlighted as another application [10].…”

mentioning

confidence: 99%

Artificial Intelligence in Medicine: Passing Hype or the Holy Grail of Solutions?

Wilhelm

Padoy

2020

Visc Med

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Artificial Intelligence in Medicine: Chances and Challenges for Wide Clinical Adoption

Cited by 59 publications

References 43 publications

Application of Deep Learning Models for Automated Identification of Parkinson’s Disease: A Review (2011–2021)

Application of Deep Learning Models for Automated Identification of Parkinson’s Disease: A Review (2011–2021)

Sensor Validation and Diagnostic Potential of Smartwatches in Movement Disorders

Artificial Intelligence in Medicine: Passing Hype or the Holy Grail of Solutions?

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