6 results about "Early disease" patented technology

This invention relates to the field of disease assessment technology, providing a method and system for disease assessment based on clinical indicators. The method includes assessing disease based on routine clinical vital signs parameters, defining normal, intermediate, and abnormal ranges for each parameter at the corresponding physiological stage based on the patient's age, establishing an abnormality judgment benchmark suitable for the individual, and avoiding the adaptation bias of general standards. Time-series data is decomposed into continuous, equal-duration minimum fluctuation units. Fluctuation characteristics are extracted and abnormal trigger units falling into the intermediate range are marked, accurately capturing early disease anomalies and overcoming the lag limitations of traditional monitoring. Abnormal trigger units are chained together according to time rules to form a transmission relationship chain. The time lag difference is calculated and linked to treatment plans to generate an emergency treatment library, reconstructing the disease development path and establishing standardized treatment comparison criteria. Real-time monitoring enables rapid alarms for critical situations, and early risk classification warnings and precise matching of treatment plans for early anomalies, improving the timeliness of disease assessment.

The invention discloses a clock drawing task-driven Alzheimer's disease early recognition method, and belongs to the technical field of disease early screening, and the method comprises the steps: collecting a static image and eight types of process signal data of a subject in a clock drawing test, and carrying out the preprocessing of the static image and eight types of process signal data; image space structure features and process signal dynamic features are extracted through a double-flow feature extraction module composed of an improved VGGNet16 network and an MLP; generating a joint feature vector through channel attention weighting and full connection layer fusion; and a polynomial loss function optimization model is adopted, and a recognition result is output through a Softmax layer. According to the method, deep fusion of static and dynamic multi-modal features is realized, key features are effectively highlighted, redundant information is inhibited, samples difficult to classify are focused, the recognition accuracy on a DARWIN data set reaches 92.59%, and the method is simple and convenient to operate, low in cost, capable of being deployed on portable equipment and suitable for clinical screening and primary medical popularization.

This application provides a system, method, and terminal for predicting the risk of bone metastasis in neuroblastoma. By acquiring laboratory test data from multiple children with neuroblastoma and constructing internal training sets, internal validation sets, external validation sets, and external test sets, a target neuroblastoma bone metastasis risk prediction model is built. Based on the target laboratory test data of the target neuroblastoma children, the system predicts the bone metastasis risk value and risk classification results for the target neuroblastoma children. This allows for timely early warning of disease changes in the early stages, reminding children and their families to seek medical attention as soon as possible. It also assists medical staff in making medical decisions and provides diagnostic evidence, solving the technical problems of limited early disease warning, high technical requirements, poor applicability, and poor safety associated with existing imaging techniques for detecting neuroblastoma bone metastasis.

The application discloses a gene combination, a kit and application thereof for early diagnosis of clear cell renal cell carcinoma, relates to the technical field of molecular diagnosis, and realizes noninvasive diagnosis by detecting the methylation level of specific genes in urine free DNA, greatly improves the accuracy of early disease identification, reduces the pain of patients and reduces the diagnosis cost, meanwhile, the noninvasive detection method is convenient to popularize and repeatedly perform, is not only suitable for initial screening, but also can continuously monitor the change of a disease in a treatment process, provides timely and reliable support for clinical decision-making, and thus effectively improves the treatment success rate and survival rate of patients.

The application discloses a watermelon fusarium wilt recognition method fusing leaf image features and a storage medium, relates to the technical field of intelligent plant disease recognition, and comprises the following steps: collecting leaf original digital images and constructing a multilevel analysis structure; different physiological state spectral components are extracted by performing multichannel spectral separation on the images, and a composite feature spectrum fusing apparent and deep physiological information is generated by using a feature growth model; the spectrum is subjected to regional deconstruction and feature matching according to the multilevel structure, a pathogen coincidence degree index of each region is calculated, and an overall infection probability mapping is fused and generated; the mapping is subjected to spatial clustering to recognize a disease spot core region, and a topological network of disease development is constructed based on the spatial morphological relationship of the disease spot core region; and a recognition conclusion is output in combination with network structure parameters and the probability mapping. The method can improve the detection sensitivity of early disease symptoms and can realize analysis of disease spatial distribution patterns and development dynamics.

This invention relates to a method and system for early disease warning and diagnosis in large-scale pig farms, belonging to the field of pig farming technology. This method utilizes knowledge graphs and abnormal features for comprehensive disease risk assessment, determining the target disease type and current disease stage of the target pigs. Based on this, an early warning is generated. An initial treatment plan is generated based on the warning information, disease type determination, and disease stage determination. On-site diagnosis and treatment are then performed on the target pigs, and samples are collected and sent for testing, forming diagnostic verification data. This invention unifies and links the initial warning record, initial diagnosis result, on-site actual treatment execution information, re-examination results, and laboratory test results of the target pigs, forming a verification data chain for individual cases. This provides a basis for subsequent verification of warning and diagnosis results, reducing the accumulation of misjudgments caused by the inability to verify model outputs alone.