US-20260128153-A1 - PROCESSING METHOD AND DEVICE OF HEMATOMA ASPIRATION DECISION-MAKING SYSTEM FOR INTRACEREBRAL HEMORRHAGE

US20260128153A1US 20260128153 A1US20260128153 A1US 20260128153A1US-20260128153-A1

Abstract

The present application provides a processing method and device of a hematoma aspiration decision-making system for intracerebral hemorrhage. A two-layer deep neural network is constructed for processing of a hematoma aspiration protocol, and a perception-decision-making-control method based on a time series is thus realized, which overcomes unpredictability of results of hematoma aspiration processes existing in the prior art, provides direct and convenient information transmission for surgeons to make decisions, can effectively improve the accuracy of aspiration treatment protocols for intracerebral hematoma, and provides digital, intelligent, and powerful support for clinical hematoma aspiration treatments.

Inventors

Zhouping Tang
CAI MENG
Diansheng CHEN
Jian Shi
Danyang Chen
Wenjie Liu
Bo Tao
Xingwei ZHAO
Chao Pan
Ping Zhang
Qing Ye

Assignees

TONGJI HOSPITAL, TONGJI MEDICAL COLLEGE, HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY

Dates

Publication Date: 20260507
Application Date: 20250512
Priority Date: 20241106

Claims (9)

1 . A processing method of a hematoma aspiration decision-making system implemented in and physically controlling a hematoma aspiration apparatus comprising a micro flowmeter, a gravimeter, a sphygmomanometer, an oximeter, a heart rate monitor, an intracranial pressure monitor, a near-infrared measuring instrument, a near-infrared probe and a micro metering pump during an intracerebral hemorrhage surgery, wherein the method comprises: acquiring preoperative data for hematoma aspiration, wherein the preoperative data for hematoma aspiration involves a first indicator; inputting the preoperative data for hematoma aspiration into a first deep neural network to determine an adapted initial hematoma aspiration protocol preoperatively, wherein the first deep neural network is configured to perform multi-center clustering on the preoperative data for hematoma aspiration input into the network and output a hematoma aspiration protocol preadapted by a most adapted clustering center; acquiring real-time intraoperative data for hematoma aspiration through the hematoma aspiration apparatus, wherein the real-time intraoperative data for hematoma aspiration involves a second indicator, the second indicator comprises an aspiration velocity, a fluid inlet velocity, blood pressure, blood oxygen, a heart rate, intracranial pressure, a hematoma location, and a hematoma size; selecting, from a plurality of pre-trained Long Short-Term Memory (LSTM) networks, a specific LSTM network that is preadapted by and corresponds to the most adapted clustering center determined by the first deep neural network; inputting the real-time intraoperative data for hematoma aspiration into a second deep neural network distinct from the first deep neural network, wherein the second deep neural network corresponds to the selected specific LSTM network, so as to determine a decision-making result of the hematoma aspiration protocol and a prediction result of the hematoma aspiration protocol-intraoperatively, wherein the second deep neural network is configured to determine, based on the real-time intraoperative data for hematoma aspiration input into the network, a time series control protocol corresponding to a real-time hematoma aspiration process, and the time series control protocol comprises protocol content of the hematoma aspiration protocol in a decision-making aspect and a prediction aspect, wherein the decision-making result comprises control amounts for a fluid inlet amount and a fluid outlet amount by the micro metering pump, and the prediction result comprises a prediction result amount and a prediction decision amount, wherein the prediction result amount comprises blood pressure, blood oxygen, a heart rate, intracranial pressure, a hematoma location, and a hematoma size, and the prediction decision amount comprises the fluid inlet amount and the fluid outlet amount; and generating, based on the decision-making result, one or more control signals that directly drive the micro metering pump of the hematoma aspiration apparatus to dynamically and physically control a fluid inlet amount through the fluid inlet channel and a fluid outlet amount through the fluid outlet channel during the surgery, wherein the micro flowmeter provides instantaneous flow-rate feedback, the gravimeter measures mass change to correct the flow-rate measurement; wherein the method implements a real-time closed-loop control of the hematoma aspiration apparatus by iteratively updating the hematoma aspiration protocol based on newly acquired intraoperative data at each subsequent moment, thereby achieving real-time and high-accuracy control of the hematoma aspiration process; for the second deep neural network, the time series control protocol being represented as: { H 1 , H 2 , … , H t , … , H T } , wherein H t is a control protocol at a moment t, T is a maximum value of t, and T corresponds to a total time, H t = { C t , S t , D t } , wherein H t comprises a number of states at the moment t, comprising a control state C t , a static state S t , and a dynamic state D t , specifically: C t serves to control an action at a next moment of the hematoma aspiration device, comprising a fluid inlet type, a fluid inlet velocity, and an aspiration velocity, S t is a descriptor value rather than a specific numerical value, serves as state monitoring, and comprises a heart rate, blood pressure, and blood oxygen, D t is used to compare with an actual value to correct a control protocol H t+1 at the next moment, comprising intracranial pressure, a hematoma size, and a hematoma location, the control protocol H t+1 at the next moment is calculated by the following equation: H t + 1 = δ [ ω i ( H t , x t ) + b i ] , wherein x t is an actual state at the moment t, comprising intracranial pressure, a hematoma size, and a hematoma location that are actually measured, δ, ω i , and b i are different network parameters of the second deep neural network, and x t and D t are used to calculate a decision error and correct a decision value at the next moment by using the decision error so as to obtain an actual decision.
2 . The method according to claim 1 , wherein the first indicator involved in the preoperative data for hematoma aspiration specifically comprises: an age, an NIHSS score, a medical history, blood pressure, blood oxygen, a heart rate, intracranial pressure, a hematoma location, a hematoma volume, a CT image, and an MRI image; and the second indicator involved in the intraoperative data for hematoma aspiration specifically comprises: an aspiration velocity, a fluid inlet velocity, blood pressure, blood oxygen, a heart rate, intracranial pressure, a hematoma location, and a hematoma size.
3 . The method according to claim 1 , wherein the protocol content of the initial hematoma aspiration protocol comprises: a tolerance range of changes in intracranial pressure, a single aspiration duration, a hematoma state, whether other drug injections are needed, and a maximum aspiration velocity.
4 . (canceled)
5 . The method according to claim 1 , wherein the method further comprises: displaying H t , C t , S t , D t , x t , and the decision error through a visual interface; receiving a control action Y t input by a decision-making participant, wherein Y t comprises at least one of changing the fluid inlet type, adjusting the fluid inlet velocity, and adjusting the aspiration velocity; and executing C t and Y t , and replacing H t with H′ t , wherein in H′ t , C′ t =C t +Y t , a remaining part is the same as H t , and a calculation mode of H t+1 is replaced with: H t + 1 = δ [ ω i ( H t ′ , x t ) + b i ] , H t ′ = { C t ′ , S t , D t } , C t ′ = C t + Y t .
6 . The method according to claim 5 , wherein the method further comprises: standardizing eigenvalues obtained by numeralization of H t , C t , S t , D t , x t , and Y t , and recording as a decision-making matrix represented as follows: x 0 x 1 … x t C 0 C 1 … C t S 0 S 1 … S t D 0 D 1 … D t Y 0 Y 1 … Y t , wherein in the decision-making matrix, each column represents all states and decision eigenvalues at a certain moment, and each row represents values of a certain feature at all moments; calculating an error between the decision-making matrix and an actual perceived value by the following equation: error =  h t - H t  , wherein h t is a decision output value of a decision-making model, and H t is an actual output value; performing gradient optimization on δ, ω i , and b i involved in the second deep neural network by using the error as a gradient direction, performing stepwise optimization on each moment, taking a finally optimized model as the second deep neural network preadapted by the most adapted clustering center, and categorizing information of a current patient in a point cluster of the most adapted clustering center, for subsequent clustering of the first deep neural network.
7 . A processing device of a hematoma aspiration decision-making system for intracerebral hemorrhage, wherein the device comprises: a processor; and a memory storing instructions that, when executed by the processor, cause the processing device to perform operations comprising: acquiring preoperative data for hematoma aspiration from one or more medical sensors or databases, wherein the preoperative data for hematoma aspiration involves a first indicator; inputting the preoperative data for hematoma aspiration into a first deep neural network to determine an adapted initial hematoma aspiration protocol preoperatively, wherein the first deep neural network is configured to perform multi-center clustering on the preoperative data for hematoma aspiration input into the network and output a hematoma aspiration protocol preadapted by a most adapted clustering center; acquiring real-time intraoperative data for hematoma aspiration through the hematoma aspiration apparatus, wherein the real-time intraoperative data for hematoma aspiration involves a second indicator the second indicator comprises an aspiration velocity, a fluid inlet velocity, blood pressure, blood oxygen, a heart rate, intracranial pressure, a hematoma location, and a hematoma size; selecting, from a plurality of pre-trained Long Short-Term Memory (LSTM) networks, a specific LSTM network that is preadapted by and corresponds to the most adapted clustering center determined by the first deep neural network; inputting the real-time intraoperative data for hematoma aspiration into a second deep neural network distinct from the first deep neural network, wherein the second deep neural network corresponds to the selected specific LSTM network, so as to determine a decision-making result of the hematoma aspiration protocol and a prediction result of the hematoma aspiration protocol intraoperatively, wherein the second deep neural network is configured to determine, based on the real-time intraoperative data for hematoma aspiration input into the network, a time series control protocol corresponding to a real-time hematoma aspiration process, and the time series control protocol comprises protocol content of the hematoma aspiration protocol in a decision-making aspect and a prediction aspect, wherein the decision-making result comprises control amounts for a fluid inlet amount and a fluid outlet amount by a micro metering pump, and the prediction result comprises a prediction result amount and a prediction decision amount, wherein the prediction result amount comprises blood pressure, blood oxygen, a heart rate, intracranial pressure, a hematoma location, and a hematoma size, and the prediction decision amount comprises the fluid inlet amount and the fluid outlet amount; and generating, based on the decision-making result, one or more control signals that directly drive the micro metering pump of a hematoma aspiration apparatus to dynamically and physically control a fluid inlet amount through the fluid inlet channel and a fluid outlet amount through the fluid outlet channel during the surgery wherein the micro flowmeter provides instantaneous flow-rate feedback, the gravimeter measures mass change to correct the flow-rate measurement; wherein the method implements a real-time closed-loop control of the hematoma aspiration apparatus by iteratively updating the hematoma aspiration protocol based on newly acquired intraoperative data at each subsequent moment, thereby achieving real-time and high-accuracy control of the hematoma aspiration process; wherein for the second deep neural network, the time series control protocol is represented as: { H 1 , H 2 , … , H t , … , H T } , wherein H t is a control protocol at a moment t, T is a maximum value of t, and T corresponds to a total time, H t = { C t , S t , D t } , wherein H t comprises a number of states at the moment t, comprising a control state C t , a static state S t , and a dynamic state D t , specifically: C t serves to control an action at a next moment, comprising a fluid inlet type, a fluid inlet velocity, and an aspiration velocity, S t is a descriptor value rather than a specific numerical value, serves as state monitoring, and comprises a heart rate, blood pressure, and blood oxygen, D t is used to compare with an actual value to correct a control protocol H t+1 at the next moment, comprising intracranial pressure, a hematoma size, and a hematoma location, the control protocol H t+1 at the next moment is calculated by the following equation: H t + 1 = δ [ ω i ( H t , x t ) + b i ] , wherein x t is an actual state at the moment t, comprising intracranial pressure, a hematoma size, and a hematoma location that are actually measured, δ, ω i , and b i are different network parameters of the second deep neural network, and x t and D t are used to calculate a decision error and correct a decision value at the next moment by using the decision error so as to obtain an actual decision.
8 . A hematoma aspiration decision-making system, comprising a processor and a memory, wherein the memory has a computer program stored therein, and the processor, when calling the computer program in the memory, executes the method according to claim 1 .
9 . A non-transitory computer-readable storage medium, wherein the computer-readable storage medium stores a plurality of instructions; and the instructions are suitable for being loaded by a processor to execute the method according to claim 1 .

Description

CROSS-REFERENCE TO RELATED APPLICATIONS The application claims priority to Chinese patent application No. 2024115702636, filed on Nov. 6, 2024, the entire contents of which are incorporated herein by reference. TECHNICAL FIELD The present application relates to the field of medical technologies, and in particular, to a processing method and device of a hematoma aspiration decision-making system for intracerebral hemorrhage. BACKGROUND Minimally invasive surgery is a new method for surgical treatments of intracerebral hemorrhage, offering certain advantages such as minimal trauma and high hematoma clearance rate. Among minimally invasive surgical treatments, the most important method is hematoma aspiration. Hematoma aspiration can be understood as a process involving: drilling through a skull, and then placing a drainage tube to perform operations such as liquefaction, aspiration, and drainage on a hematoma, so as to reduce the hematoma and lower intracranial pressure. In current clinical treatments, the hematoma aspiration method mainly relies on experience of clinicians. First, a surgeon determines an aspiration protocol based on preoperative signs and indicators of a patient such as an age, a hematoma site, a hematoma size, and preoperative images; and during an aspiration process, the surgeon relies on experience to control an aspiration velocity, ultimately achieving the purpose of reducing hematoma and lowering intracranial pressure. However, the hematoma aspiration method undergoes a long operation time, and the control of the aspiration process is entirely dependent on personal experience of the surgeon. During a long period of surgery, the aspiration effect cannot be guaranteed, and the following problems may easily occur: (1) incomplete aspiration: during the hematoma aspiration process, the hematoma is deformed due to aspiration, and residual hematoma fails to be absorbed, resulting in deterioration of intracerebral hemorrhage condition; (2) over-aspiration: as changes of the hematoma cannot be predicted during the aspiration process, over-aspiration may occur, which causes damages to normal brain tissue and affect normal brain functions; and (3) unstable control of the aspiration process: the aspiration process causes acute fluctuations in intracranial pressure, which affects the pressure balance and potentially causes vascular rupture, etc., leading to a secondary damage to the brain tissue. In view of this, there exists a demand for applying digital technologies to assist hematoma aspiration. The inventors of the present application found that by adopting existing solutions, only part of functions can be implemented, but for example, real-time monitoring of key information during hematoma aspiration is neglected, treatment protocols are merely recommended based on preoperative information, and digital control and intelligent real-time decision-making cannot be achieved during the hematoma aspiration process. For another example, the existing solutions only focus on real-time situations during the hematoma aspiration process and lack real-time prediction, making it difficult to control impending abnormalities. That is to say, the prior art is still in a relatively primitive stage for how to assist hematoma aspiration based on the digital technologies, fails to reach a deep level of intelligent processing effect, and still lacks practical value. SUMMARY The present application provides a processing method and device of a hematoma aspiration decision-making system for intracerebral hemorrhage. A two-layer deep neural network is constructed for processing of a hematoma aspiration protocol, and a perception-decision-making-control method based on a time series is thus realized, which overcomes unpredictability of results of hematoma aspiration processes existing in the prior art, provides direct and convenient information transmission for surgeons to make decisions, can effectively improve the accuracy of aspiration treatment protocols for intracerebral hematoma, and provides digital, intelligent, and powerful support for clinical hematoma aspiration treatments. In a first aspect, the present application provides a processing method of a hematoma aspiration decision-making system for intracerebral hemorrhage. The method includes: acquiring preoperative data for hematoma aspiration, where the preoperative data for hematoma aspiration involves a first indicator;inputting the preoperative data for hematoma aspiration into a first deep neural network to determine an adapted initial hematoma aspiration protocol preoperatively, where the first deep neural network is configured to perform multi-center clustering on the preoperative data for hematoma aspiration input into the network and output a hematoma aspiration protocol preadapted by a most adapted clustering center;acquiring real-time intraoperative data for hematoma aspiration, where the real-time intraoperative data for hematoma aspiration involves