ki:elements
pioneers in speech biomarkers
ki: künstliche intelligenz (German, artificial intelligence)
our mission = your unmet clinical needs
getting assets faster to orbit & exploring uncharted terrain
ki:elements pioneers speech-based assessments for neurological & psychiatric pathologies, modelling approved biomarkers.
We model symptoms
We model symptoms
- that are not measured remotely or at such high sampling rate
- that today receive no objective signal of the same quality
our offering = your steps towards a successful mission
fueling neuroscience in four stages
consulting
- Identification of use cases and research opportunities
- Technical feasibility
- Conceptual development of speech biomarkers
research
- Data science on speech data
- Discovery of new speech biomarkers for multiple pathologies
- Design and support of speech-based studies
development
- Speech biomarker development for in-use-case application
- Creation of production-ready speech pipelines
commercialisation
- Licensing use-case ML models and API for new products
- Licensing of validated speech biomarkers
our engine = your scientifically valid solution
Detecting symptoms through linguistic and paralinguistic (acoustic) processing of speech.
- alzheimer's disease
- multiple sclerosis
-
depression
- parkinson's disease
Screening for AD biomarker noninvasively
Input
connected speech from story retelling
throughput
syntactic complexity + semantic units/story keywords
output
language & episodic memory impairment markers
Differentiating between cognitive syndrome and fatigue.
Input
verbal fluency recording (VF)
throughput
pitch & prosody + VF temporal & linguistic clustering
output
fatigue + dysexectuive syndrome markers
Disease course and relapse prognosis
Input
free connected speech from speech diary entries
throughput
entriessentiment + keywords + acoustic microvariations,
pitch & prosody
output
suicide risk and relapse markers
Differentiating between motor speech problem and cognitive language problems
Input
free connected speech
throughput
pitch & loudness variations + microvariations + syntactic complexity
output
tremor + language markers
alzheimer's disease
Screening for AD biomarker noninvasively
Input
connected speech from story retelling
throughput
syntactic complexity + semantic units/story keywords
output
language & episodic memory impairment markers
multiple sclerosis
Differentiating between cognitive syndrome and fatigue.
Input
verbal fluency recording (VF)
throughput
pitch & prosody + VF temporal & linguistic clustering
output
fatigue + dysexectuive syndrome markers
depression
Disease course and relapse prognosis
Input
free connected speech from speech diary entries
throughput
entriessentiment + keywords + acoustic microvariations,
pitch & prosody
output
suicide risk and relapse markers
parkinson's disease
Differentiating between motor speech problem and cognitive language problems
Input
free connected speech
throughput
pitch & loudness variations + microvariations + syntactic complexity
output
tremor + language markers
our ai = your clinical research
launching truly interdisciplinary missions
At ki: we believe in the value of interdisciplinary missions. Constantly pushing the limits at the juncture of applied artificial intelligence, natural language processing and neuroscience, interdisciplinary research is in our DNA. Why care?
- transparency for improved compliance of your solutions
- clinical explainability down to a symptom level
- validity through solid research #rocketscience
our trajectory = your clinical value add
exploring a whole space of opportunities
- alzheimer's disease
- affective disorders
- multiple sclerosis
-
schizophrenia
- parkinson's disease
Screening
- Screening for ADRD biomarker noninvasively
- Screening for affective disturbances as risk factors
diagnosis
- Differential diagnosis to other dementia causes (e.g. FTLD)
- Differential diagnosis to pseudo-dementia due to affective disorders
monitoring
- Low-threshold monitoring of cognition and mood
- Predicting conversion probability from MCI to AD trough monitoring
Screening
- Hypomanic eposides in individuals at risk
- Low threshold screening for depressive symptoms
diagnosis
- Differential diagnosis between unipolar depression and bipolar disorder
- Characterization of cognitive symptoms in mania
monitoring
- Stratification of different bipolar subforms by tracking of symptoms
- Disease, course and relapse prognosis
Screening
- Although, screening and early detection for MS is of high importance to avoid later impairments, we do not see early symptoms that can be measured through speech
diagnosis
- Differentiating between cognitive syndrome and fatigue
- Differentiating between impaired language (cognition) and impaired speech (dysarthria)
monitoring
- Stratification of different MS subforms by tracking of symptoms
- Prognosis of future episodes
Screening
- Negative symptom detection in prodromal phase within (ultra) high risk subjects
diagnosis
- Objective classification of formal thought disorder
monitoring
- Negative symptom monitoring during remission
- Disease course and relapse prognosis
- Monitoring of formal thought disorder
Screening
- Screening for cognitive symptoms as a precursor of motor symptoms
diagnosis
- Differentiating between motor speech problem and cognitive language problems
monitoring
- Managing progressive disease course by monitoring treatment response
- Predicting dementia risk
alzheimer's disease
Screening
- Screening for ADRD biomarker noninvasively
- Screening for affective disturbances as risk factors
diagnosis
- Differential diagnosis to other dementia causes (e.g. FTLD)
- Differential diagnosis to pseudo-dementia due to affective disorders
monitoring
- Low-threshold monitoring of cognition and mood
- Predicting conversion probability from MCI to AD trough monitoring
affective disorders
Screening
- Hypomanic eposides in individuals at risk
- Low threshold screening for depressive symptoms
diagnosis
- Differential diagnosis between unipolar depression and bipolar disorder
- Characterization of cognitive symptoms in mania
monitoring
- Stratification of different bipolar subforms by tracking of symptoms
- Disease, course and relapse prognosis
multiple sclerosis
Screening
- Although, screening and early detection for MS is of high importance to avoid later impairments, we do not see early symptoms that can be measured through speech
diagnosis
- Differentiating between cognitive syndrome and fatigue
- Differentiating between impaired language (cognition) and impaired speech (dysarthria)
monitoring
- Stratification of different MS subforms by tracking of symptoms
- Prognosis of future episodes
schizophrenia
Screening
- Negative symptom detection in prodromal phase within (ultra) high risk subjects
diagnosis
- Objective classification of formal thought disorder
monitoring
- Negative symptom monitoring during remission
- Disease course and relapse prognosis
- Monitoring of formal thought disorder
parkinson's disease
Screening
- Screening for cognitive symptoms as a precursor of motor symptoms
diagnosis
- Differentiating between motor speech problem and cognitive language problems
monitoring
- Managing progressive disease course by monitoring treatment response
- Predicting dementia risk
our running.missions = your reassurance
generating clinically actionable insights
- apathy
apathy
our crew = your onboarding opportunity
working together as a team
Nicklas
Linz
CEO
data Scientist
Caroline
Lutz
CCO
business developer
Johannes
Tröger
CSO
psychologist
Janna
Herrmann
CTO
developer
Dr. Duy Truong
data scientist
Mario
Mina
data Scientist
Elisa
Mallick
data scientist
Louisa
Schwed
data scientist
Jannic
Warken
developer
Bahar Tarakameh
developer
Simona Schäfer
scientist
Sina
Lambing
Marketing
Tanja
Leister
team assistance
