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The unmet timely diagnosis requirements, that take place years after substantial neural loss and neuroperturbations in neuropsychiatric disorders, affirm the dire need for biomarkers with proven efficacy. In Parkinson’s disease (PD), Mild Cognitive impairment (MCI), Alzheimers disease (AD) and psychiatric disorders, it is difficult to detect early symptoms given their mild nature. We hypothesize that employing fine motor patterns, derived from natural interactions with keyboards, also knwon as keystroke dynamics, could translate classic finger dexterity tests from clinics to populations in-the-wild for timely diagnosis, yet, further evidence is required to prove this efficiency. We have searched PubMED, Medline, IEEEXplore, EBSCO and Web of Science for eligible diagnostic accuracy studies employing keystroke dynamics as an index test for the detection of neuropsychiatric disorders as the main target condition. We evaluated the diagnostic performance of keystroke dynamics across 41 studies published between 2014 and March 2022, comprising 3791 PD patients, 254 MCI patients, and 374 psychiatric disease patients. Of these, 25 studies were included in univariate random-effect meta-analysis models for diagnostic performance assessment. Pooled sensitivity and specificity are 0.86 (95% Confidence Interval (CI) 0.82–0.90, I2 = 79.49%) and 0.83 (CI 0.79–0.87, I2 = 83.45%) for PD, 0.83 (95% CI 0.65–1.00, I2 = 79.10%) and 0.87 (95% CI 0.80–0.93, I2 = 0%) for psychomotor impairment, and 0.85 (95% CI 0.74–0.96, I2 = 50.39%) and 0.82 (95% CI 0.70–0.94, I2 = 87.73%) for MCI and early AD, respectively. Our subgroup analyses conveyed the diagnosis efficiency of keystroke dynamics for naturalistic self-reported data, and the promising performance of multimodal analysis of naturalistic behavioral data and deep learning methods in detecting disease-induced phenotypes. The meta-regression models showed the increase in diagnostic accuracy and fine motor impairment severity index with age and disease duration for PD and MCI. The risk of bias, based on the QUADAS-2 tool, is deemed low to moderate and overall, we rated the quality of evidence to be moderate. We conveyed the feasibility of keystroke dynamics as digital biomarkers for fine motor decline in naturalistic environments. Future work to evaluate their performance for longitudinal disease monitoring and therapeutic implications is yet to be performed. We eventually propose a partnership strategy based on a “co-creation” approach that stems from mechanistic explanations of patients’ characteristics derived from data obtained in-clinics and under ecologically valid settings. The protocol of this systematic review and meta-analysis is registered in PROSPERO; identifier CRD42021278707. The presented work is supported by the KU-KAIST joint research center.

Neurologists nowadays no longer view neurodegenerative diseases, like Parkinson’s and Alzheimer’s disease, as single entities, but rather as a spectrum of multifaceted symptoms with heterogeneous progression courses and treatment responses. The definition of the naturalistic behavioral repertoire of early neurodegenerative manifestations is still elusive, impeding early diagnosis and intervention. Central to this view is the role of artificial intelligence (AI) in reinforcing the depth of phenotypic information, thereby supporting the paradigm shift to precision medicine and personalized healthcare. This suggestion advocates the definition of disease subtypes in a new biomarker-supported nosology framework, yet without empirical consensus on standardization, reliability and interpretability. Although the well-defined neurodegenerative processes, linked to a triad of motor and non-motor preclinical symptoms, are detected by clinical intuition, we undertake an unbiased data-driven approach to identify different patterns of neuropathology distribution based on the naturalistic behavior data inherent to populations in-the-wild. We appraise the role of remote technologies in the definition of digital phenotyping specific to brain-, body- and social-level neurodegenerative subtle symptoms, emphasizing inter- and intra-patient variability powered by deep learning. As such, the present review endeavors to exploit digital technologies and AI to create disease-specific phenotypic explanations, facilitating the understanding of neurodegenerative diseases as “bio-psycho-social” conditions. Not only does this translational effort within explainable digital phenotyping foster the understanding of disease-induced traits, but it also enhances diagnostic and, eventually, treatment personalization.

While the dilemma of motion tracking and force control in beating-heart surgery is previously addressed using active control architectures and rigid robotic actuators, this work leverages the highly controllable mechanical properties of concentric tube robots for intelligent, design-based force control in minimally invasive cardiac ablation. Briefly, cardiac ablation is the conventional procedure for treating arrhythmia patients, by which exposing the diseased cardiac tissue to Radio-Frequency (RF) energy restores the normal heart rhythm. Yet, the procedure suffers low success rate due to the inability of existing flexible catheters to maintain a consistent, optimal contact force between the tip electrode and the tissue, imposing the need for future repeat surgeries upon disease recurrence. The novelty of our work lies in the development of a statically-balanced compliant mechanism composed of (1) distal bi-stable concentric tubes and (2) a compliant, torsional spring mechanism that provides torque at tubes proximal extremity, resulting in an energy-free catheter with a zero-stiffness tip. This catheter is expected to maintain surgical efficacy and safety despite the chaotic displacement of the heart, by naturally keeping the tip force at an optimal level, not less and not more than the surgical requirement. The presented experimental results of the physical prototype, reflect the feasibility of the proposed design, as well as the robustness of the formulated catheter mathematical models which were uniquely deployed in the selection of the optimal design parameters.

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