Clinical Diagnosis Automation
Clinical Diagnosis Automation - Related Videos
10:24
Automated Cell Enrichment of Cytomegalovirus-specific T cells for Clinical Applications using the Cytokine-capture System
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Cited by 23 •
2015
The goal of this protocol is to manufacture pathogen-specific clinical-grade T cells using a bench-top, automated, second generation cell enrichment device that incorporates a closed cytokine capture system and does not require dedicated staff or use of a GMP facility. The cytomegalovirus pp65-specific-T cells generated can be directly administered to patients.
01:25
Type II Diabetes Mellitus III: Clinical Manifestations and Diagnosis
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2026
Type 2 diabetes mellitus develops gradually and is often asymptomatic in early stages.Clinical ManifestationsWhen symptoms appear, they include fatigue, blurred vision, pruritus, delayed wound healing, and recurrent infections, particularly candidal infections. Peripheral neuropathy may present as numbness or tingling in the extremities. Classic hyperglycemia symptoms—polyuria, polydipsia, and polyphagia—are less common. Most patients are overweight and frequently have associated hypertension...
07:34
FISH for Pre-implantation Genetic Diagnosis
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Cited by 17 •
2011
This article describes the selection of suitable probes for single-cell FISH, spreading techniques for blastomere nuclei, and in situ hybridization and signal scoring, applied to pre-implantation genetic diagnosis (PGD) in a clinical setting.
08:43
Diagnosis of Musculus Gastrocnemius Tightness - Key Factors for the Clinical Examination
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Cited by 12 •
2016
Isolated Musculus gastrocnemius tightness is a common cause for foot and ankle pathologies. Currently no standardized examination procedure exists. This manuscript demonstrates that 20 degree of knee flexion eliminates the restraining effect of the M. gastrocnemius on ankle dorsiflexion and presents a video description of a standardized examination protocol.
10:47
High-throughput, Automated Extraction of DNA and RNA from Clinical Samples using TruTip Technology on Common Liquid Handling Robots
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Cited by 13 •
2013
TruTip is a simple nucleic acid extraction technology whereby a porous, monolithic binding matrix is inserted into a pipette tip. Consequently, the sample preparation format is compatible with most liquid handling instruments, and can be used for many medium to high-throughput clinical applications and sample types.
FAQs
Rule-based systems apply explicitly programmed logic to clinical information, while machine-learning models identify patterns within available data and use them to estimate diagnostic probabilities. This distinction affects how the system is developed and interpreted: programmed rules can be inspected directly, whereas learned patterns require careful validation and suitable explanation. Both approaches can support clinical decision-making when integrated with appropriate oversight.
The available data shape what an automated system can analyze and how it supports diagnosis. Symptoms and laboratory results provide clinical indicators, medical images contribute visual information, and electronic health records supply broader patient information. Processing these sources requires software development, data science, and biomedical signal processing so that relevant patterns can be detected and presented in a clinically useful form.
Validation helps determine whether a system performs reliably for its intended diagnostic support task, while interpretability helps clinicians understand the basis or meaning of its outputs. These safeguards matter because automated results may influence screening, triage, or workflow decisions. Engineering teams therefore need to assess system behavior and make its decision support understandable enough for responsible clinical use.
Privacy protects the clinical information processed by automation, and oversight keeps clinicians involved in judging how outputs should affect patient care. Human-centered design focuses the tool on real clinical workflows rather than on computation alone. Together, these principles support safer integration, help address usability concerns, and contribute to more equitable care across the populations served.
Integration begins by selecting relevant clinical data, such as symptoms, images, laboratory results, or electronic health records, and then applying rule-based logic or a machine-learning model to analyze them. The system presents detected patterns or diagnostic probabilities as decision support. Clinicians can then incorporate those outputs into screening, triage, or workflow activities while maintaining appropriate oversight.
These systems are relevant when clinical organizations need computational support for reviewing patient information and identifying patterns associated with diseases or health conditions. Screening can help organize attention around available clinical data, triage can support prioritization, and workflow tools can present information within care processes. Their suitability depends on validation, privacy protections, interpretability, and responsible clinical integration.
Engineering this technology combines software development, data science, biomedical signal processing, and human-centered design. Software development supports the computational system, data science contributes methods for analyzing information, and biomedical signal processing addresses clinically relevant signals. Human-centered design connects the resulting tool to clinicians and workflows, helping ensure that technical outputs remain usable for decision support.