# US 10,120,980 B2 · Diagnosing pulmonary embolism by integrating patient-level diagnosis and embolus-level detection

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- Number: US 10,120,980 B2
- Document ID: US10120980B2
- Status: Granted
- Kind: B2
- Application number: 15/114,164
- Filed: 2015-01-27
- Published: 2017-01-05
- Granted: 2018-11-06
- Earliest claimed: 2014-01-27
- Assignees: Arizona Board of Regents on behalf of Arizona State University
- Applicants: Jianming Liang
- Inventors: Jianming Liang
- Web page: https://patentgrep.com/patent/US10120980B2

## CPC

- G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems
- A61B 5/08: Measuring devices for evaluating the respiratory organs
- A61B 5/7267: {involving training the classification device}
- A61B 6/50: specially adapted for specific body parts; specially adapted for specific clinical applications
- A61B 6/5217: {extracting a diagnostic or physiological parameter from medical diagnostic data}
- G16Z 99/00: Subject matter not provided for in other main groups of this subclass
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- G16Z: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS, NOT OTHERWISE PROVIDED FOR

## Abstract

Improved pulmonary embolism (PE) detection may be obtained through computer aided-diagnosis. In particular, PE detection may be accomplished through patient-level diagnosis, embolus-level detection, or a combination of the two. Patient-level diagnosis operates to quickly exclude non-PE patients and dispatch PE-patients to treatment. Embolus-level detection operates to localize individual emboli to support personalized medicine via risk stratification. Multiple instance-based learning (MIBL) classification at the patient level explores the key observation that once any TP candidate of a patient is classified as positive, the patient is identified as PE positive. That is, MIBL focuses on correct classification of patients rather than individual candidates, to effectively and rapidly distinguish between PE patients and non-PE patients.

## Claims

### Claim 1

A method, comprising:
receiving candidates for classification as true positives and false positives, wherein the candidates are associated with one of a plurality of groups;
determining a classification boundary for the true positives and the false positives;
calculating hinge errors for the received candidates based, at least in part, on the determined classification boundary;
setting a portion of the calculated hinge errors to zero, wherein the portion comprises hinge errors corresponding to candidates on a false positive side of the determined classification boundary belonging to a group of the plurality of groups also having candidates on a true positive side of the determined classification boundary;
determining a shifted classification boundary based, at least in part, on the hinge errors after setting the portion of the calculated hinge errors to zero; and
identifying as true positives the candidates on a true positive side of the shifted classification boundary.

### Claim 2 (depends on 1)

The method of claim 1, wherein the step of determining a shifted classification boundary comprises applying a support vector machine (SVM) to construct a linear classification function by minimizing the hinge errors.

### Claim 3 (depends on 2)

The method of claim 2, wherein the step of constructing the linear classification function comprises constructing the linear classification function such that a minimum hinge error for candidates associated with each group of the plurality of groups is zero.

### Claim 4 (depends on 2)

The method of claim 2, wherein the step of minimizing the hinge errors comprises solving the optimization problem defined by
minw,ξ,λ γ∥w∥1+Σp=1n(Σi∈Ip+λiξi)+Σi∈I−ξi
s.t. wTxi+b≥1−ξi, i∈I+,
wTxi+b≤−1+ξi, i∈I−,
ξi≥0, i=1, . . . , l,
Σi∈Ip+λi=1, λi≥0, i∈Ip+, p=1, . . . , n.

### Claim 5 (depends on 2)

The method of claim 2, wherein the linear classification function is of the form wTx+b and wherein the hinge error is defined as max{0,1−y (wTx−b)}.

### Claim 6 (depends on 1)

The method of claim 1, further comprising aggregating multiple classifiers to reduce a variance of the determination of the classification boundary over multiple received candidates.

### Claim 7 (depends on 1)

The method of claim 1, wherein the plurality of groups corresponds to a plurality of patients.

### Claim 8 (depends on 7)

The method of claim 7, further comprising identifying a portion of the plurality of patients having a pulmonary embolism, wherein each patient of the identified portion has at least one candidate identified as a true positive.

### Claim 9 (depends on 7)

The method of claim 7, further comprising:
building a second set of candidates by selecting, from the received candidates, candidates corresponding to the portion of the plurality of patients identified as having a pulmonary embolism, wherein the second set of candidates corresponds to a plurality of emboli;
determining a second classification boundary for the second set such that a minimum hinge error for candidates of the second set of candidates associated with each embolism of the plurality of emboli is zero; and
identifying as true positives the candidates on a true positive side of the second classification boundary.

### Claim 10 (depends on 9)

The method of claim 9, wherein the step of determining the second classification boundary comprises determining a second shifted classification boundary comprising: determining a classification boundary for true positives and false positives; calculating hinge errors for the second set of candidates based, at least in part, on the determined classification boundary; setting a portion of the calculated hinge errors to zero, wherein the portion comprises hinge errors corresponding to candidates on a false positive side of the determined classification boundary corresponding to embolisms also having candidates on a true positive side of the determined classification boundary; determining the second shifted classification boundary based, at least in part, on the hinge errors after setting the portion of the calculated hinge errors to zero.

### Claim 11 (depends on 1)

The method of claim 1, wherein the plurality of groups corresponds to a plurality of pulmonary emboli.

### Claim 12

A computer program product, comprising:
a non-transitory computer readable medium comprising code to perform the steps of:
receiving candidates for classification as true positives and false positives, wherein the candidates are associated with one of a plurality of groups;
determining a classification boundary for the true positives and the false positives;
calculating hinge errors for the received candidates based, at least in part, on the determined classification boundary;
setting a portion of the calculated hinge errors to zero, wherein the portion comprises hinge errors corresponding to candidates on a false positive side of the determined classification boundary belonging to a group of the plurality of groups also having candidates on a true positive side of the determined classification boundary;
determining a shifted classification boundary based, at least in part, on the hinge errors after setting the portion of the calculated hinge errors to zero; and
identifying as true positives the candidates on a true positive side of the shifted classification boundary.

### Claim 13 (depends on 12)

The computer program product of claim 12, wherein the plurality of groups corresponds to a plurality of patients.

### Claim 14 (depends on 13)

The computer program product of claim 13, wherein the medium further comprises code to perform the step of identifying a portion of the plurality of patients having a pulmonary embolism, wherein each patient of the identified portion has at least one candidate identified as a true positive.

### Claim 15 (depends on 12)

The computer program product of claim 12, wherein the plurality of groups corresponds to a plurality of pulmonary emboli.

### Claim 16

An apparatus, comprising:
a memory; and
a processor coupled to the memory, wherein the processor is configured to perform the steps of:
receiving candidates for classification as true positives and false positives, wherein the candidates are associated with one of a plurality of groups;
determining a classification boundary for the true positives and the false positives;
calculating hinge errors for the received candidates based, at least in part, on the determined classification boundary;
setting a portion of the calculated hinge errors to zero, wherein the portion comprises hinge errors corresponding to candidates on a false positive side of the determined classification boundary belonging to a group of the plurality of groups also having candidates on a true positive side of the determined classification boundary;
determining a shifted classification boundary based, at least in part, on the hinge errors after setting the portion of the calculated hinge errors to zero; and
identifying as true positives the candidates on a true positive side of the shifted classification boundary.

### Claim 17 (depends on 16)

The apparatus of claim 16, wherein the plurality of groups corresponds to a plurality of patients.

### Claim 18 (depends on 17)

The apparatus of claim 17, wherein the processor is further configured to perform the step of identifying a portion of the plurality of patients having a pulmonary embolism, wherein each patient of the identified portion has at least one candidate identified as a true positive.

### Claim 19 (depends on 16)

The apparatus of claim 16, wherein the plurality of groups corresponds to a plurality of pulmonary emboli.