Contactless medical equipment AI big data risk control and quasi thinking iterative planning

$$mathrm{Assume }delta =center_frequency/imagein{g}_{frequency}, andomega =left(TRotimes TEright), mathrm{MR}=mathrm{Image Definition} .$$

Image definition formula, diagonal matrix signal transmission and reception form. Therefore, to some extent, sometimes its magnetic resonance signals can be received and interpreted6.

$${A}^{left(x,y,zright)}to frac{delta }{omega }times {Matrixleft[begin{array}{ccc}{E}_{x}& & \ & {S}_{y}& \ & & {M}_{z}end{array}right]},and {A}^{left(x,y,zright)}to Imag{e}_{Definite}$$

(4)

The general formula of MRI image definition is as follows:

$$begin{aligned} & left( {A_{{left( {x,y,z} right)}}^{MR} ,overline{{A_{{left( {x,y, z} right)}}^{MR} }} } right)^{{H_{ij} Q_{i} H_{ji}^{H} }} = \ & quad mathop sum limits_ {i = 1}^{k} frac{{varvec{delta}}}{{omega_{i} }} times logleft| {I + R^{ – 1} times H_{ij} times Matrixleft[ {begin{array}{*{20}c} {E_{x} } & {} & {} \ {} & {S_{y} } & {} \ {} & {} & {M_{z} } \ end{array} } right]_{i}^{Q} left( {A_{{}}^{E,S,M} ,overline{{A_{{}}^{E,S,M} }} } right) times H_{ji}^{H} } right|,and \ & quad R^{ – 1} interference signal, \ & quad E_{x} = Excitations_number,S_{y} = Spacing _between_slices,M_{z} = Magnet_field_strength, \ & quad omega_{i} = left( {TR otimes TE} right) \ end{aligned}$$

(5)

Therefore, the image definition of MR is directly related to the interference signal (({R}^{-1})). It is also related to the performance of MR machine, that is, whether it is high-end MR. The image of high-dimensional signal (information polar coordinates) of MR DISCOVERY MR750w is as follows, and reference to Figs. 14, 15.

Figure 14

MR image definition heavy core clustering tanh balanced big data risk control high-dimensional data polar coordinates graph (2021-09-15 16:52:02).

Figure 15
figure 15

MR image definition heavy core clustering tanh balanced big data risk control high-dimensional data polar coordinates graph (2021-09-18 06:40:04).

({omega }_{i}=left(TRotimes TEright)) is a constraint parameter. (1/{omega }_{i}) controls the stability morphological characteristics of high-dimensional information distribution boundary, and its image is as above. The core energy and sub core energy structure Q of MR,({Q}_{core}=Eleft{{X}_{k}{X}_{k}^{H}right})

$$begin{aligned} & Q_{core}^{{}} left( {A_{{}}^{{X_{E} ,X_{S} ,X_{M} }} ,overline{{ A_{{}}^{{X_{E} ,X_{S} ,X_{M} }} }} } right) = Matrixleft[ {begin{array}{*{20}c} {E_{{X_{E} }}^{k} otimes X_{k}^{H} } & {} & {} \ {} & {E_{{X_{S} }}^{k} otimes X_{k}^{H} } & {} \ {} & {} & {E_{{X_{M} }}^{k} otimes X_{k}^{H} } \ end{array} } right]_{i}^{Q} ,and E_{{X_{E} }}^{k} otimes X_{k}^{H} ,E_{{X_{S} }}^{k} otimes X_ {k}^{H} ,E_{{X_{M} }}^{k} \ & quad otimes X_{k}^{H} {text{Sub core energy structure}} \ end {aligned}$$

(6)

The simplified general formula for MR image definition is as follows:

$$begin{aligned} & left( {A_{{left( {x,y,z} right)}}^{core} ,overline{{A_{{left( {x,y, z} right)}}^{core} }} } right)_{MR}^{{H_{ij} Q_{i} H_{ji}^{H} }} = mathop sum limits_{ i = 1}^{k} frac{delta }{{omega_{i} }} times logleft| {I + R^{ – 1} times H_{ij} times Q_{core}^{{}} left( {A_{{}}^{{X_{E} ,X_{S} ,X_{ M} }} ,overline{{A_{{}}^{{X_{E} ,X_{S} ,X_{M} }} }} } right) times H_{ji}^{H} } right| \ & quad ,and R^{ – 1} Interference signal,omega_{i} = left( {TR otimes TE} right) \ end{aligned}$$

(7)

When the ({{varvec{R}}}^{-1}) interference signal is strengthened, the clarity of MR image decreases and the comprehensive evaluation index decreases

The MR parameter is related to the machine parameter (upomega =left(mathrm{TR}otimes mathrm{TE}right)), excitations_number, spacing_between_slices, Magnet_field_strength, SAR. Reference to Figs. 16, 17 and 18.

Figure 16
figure 16

MR image definition and performance were 54.398% respectively.

Figure 17
figure 17

MR image definition and performance were 41.551% respectively.

Figure 18
figure 18

MR image definition and performance were 45.473% respectively. Comprehensive evaluation indexes: 54.398%, 41.551%, 45.473%, its core boundary is 40.01%, and the scientifically of the image are reduced.

When ({mathbf{R}}^{-1}) interference signals decrease, MR image clarity increases and comprehensive evaluation index increases

Comprehensive evaluation indexes: 69.730%, 62.940%, 74.716%, its core boundary is 40.01%, and the image is more scientific. And reference to Figs. 19, 20, 21.

Figure 19
figure 19

MR image definition and performance were 69.730% respectively.

Figure 20
figure 20

MR image definition and performance were 62.940% respectively.

Figure 21
figure 21

MR image definition and performance were 74.716% respectively.

MR peak SAR RF (similar to CT exposure time high-dimensional data heavy core clustering mathematical model)

If SAR > 11.2 then MR stops, when SAR drops down, start MR again. MR does not need to set the domain value, because AI Mathematical model risk control can dynamically find the domain value and boundary of various internal indicators of MR machine. This is the advantage of AI system, and adopts the most cutting-edge and advanced original innovative mathematics to combine with AI. Medical equipment management is characterized by high professionalism, high compliance requirements, diverse types and uses, scattered applicable standards and regulations, and large time and space span of equipment management7.

AI Mathematical model risk control can automatically and dynamically find the domain values ​​and boundaries of various medical equipment indexes, such as the domain values ​​and boundaries of CT’s heat capacity and internal indexes of the machine. And reference to Figs. 22, 23.

Figure 22
figure 22

Heat capacity machine internal index and domain value of CT [weight kernel clustering tanh equilibrium big data risk control high-dimensional data] polar graph (2021-09-19 07:36:05).

Figure 23
figure 23

Heat capacity machine internal index and domain value of CT [weight kernel clustering tanh equilibrium big data risk control high-dimensional data] polar graph (2021-09-14 16:59:04).

AI Mathematical model risk control automatically and dynamically finds the index domain values ​​and boundaries of various medical equipment. Such as MR peakSAR RF, image definition, internal index domain value and boundary of the machine. And reference to Figs. 24, 25.

Figure 24
figure 24

Machine internal index domain value of MR image clarity measurement [weight kernel clustering tanh equilibrium big data risk control high-dimensional data] polar graph (2021-09-28 04:14:08).

Figure 25
figure 25

Machine internal index domain value of MR image clarity measurement [weight kernel clustering tanh equilibrium big data risk control high-dimensional data] polar graph (2021-09-15 16:52:02).

Application scenario of non super flat enhanced heavy core TANH equilibrium state

Analyze the stability of DISCOVERY MR750w equipment. AI Mathematical model risk control big data found that the ductility, generality and high reliability of MR equipment DISCOVERY MR750w are also an important basis for judging whether it is a high-end MR. The reliability boundary is 40.01%, and refer to Figs. 26, 27, 28, which also reflects another important basis for high-end MR. MR peak SAR RF (core data of heavy core clustering TANH equilibrium state is similar to CT exposure time), high-dimensional signal image, and AI Mathematical model risk control image similar to CT exposure time.

Figure 26
figure 26

MR peak SAR heavy core clustering tanh balanced big data risk control high-dimensional data polar coordinates graph (2021-09-28 04:14:44).

Figure 27
figure 27

MR peak SAR heavy core clustering tanh balanced big data risk control high-dimensional data polar coordinates graph (2021-09-27 04:29:41).

Figure 28
figure 28

MR peak SAR heavy core clustering tanh balanced big data risk control high-dimensional data polar coordinates graph (2021-09-26 04:24:38).

New generations of medical AI big data platform based on heavy core clustering quasi thinking iterative planning

Capture the most important quasi thinking wave curve (signal), and through the vibration of random function and AI operation, iterate and determine the condition, namely domain value. If possible, the fluctuation curve of human (thinking) brain wave signal, that is, the iterative evolution of brain like AI form on the reliability of risk control of the above large medical equipment from weak to strong8, can be used to provide a basis for obtaining risk control of CT large equipment. Reliability percentage data of risk control of large medical equipment are analyzed by long-time distribution curve. It can be learned and trained by KNN of AI neural network. Moreover, the heavy core data corresponding to this reliability < +[1, 10]—[1, 10]> is KNN of dual core neural network, and the correct risk control successful data are marked through unsupervised learning.

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