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Checking Tuberculosis drug adherence In patients

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Vaishnavi Venkat

on 12 June 2014

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Transcript of Checking Tuberculosis drug adherence In patients

Checking Tuberculosis
Drug Adherence
in Patients

Team Members:

Shivashree D. Raj
V. Vaishnavi
Project Place:
Bigtec Labs
External Guide:
Dr. Phani Kumar
Internal Guide:
Dr. Deepak R
PROBLEM STATEMENT
What is Tuberculosis?
Mycobacterium tuberculosis Infection
Adherence Treatment
OBJECTIVES:
To survey existing methods to check the drug adherence in patients
To list out the benefits and drawbacks of each effects
To develop a new method to check drug adherence
To develop a prototype of a mobile application to support this method
To test the efficiency of the developed mobile application

Literature Survey
World Statistics
Tuberculosis Drug
Adherence Studies
conducted:
Pablos-Méndez A, Knirsch CA, Barr RG, et al. Nonadherence in tuberculosis treatment:
predictors and consequences in New York City. Am J Med 1997; 102:164.
In one retrospective study including 184 patients with TB in New York City (nearly half of whom were nonadherent), the
nonadherent patients took longer to convert to negative culture results
(254 versus 64 days),
were more likely to acquire drug resistance
(relative risk 5.6),
and required longer treatment regimens
(560 versus 324 days)
Frederick AD Kaona1*, Mary Tuba1, Seter Siziya2 and Lenganji Sikaona3, An assessment of factors contributing to treatment adherence and knowledge of TB transmission among patients on TB treatment, BMC Public Health. 2004; 4: 68, Published online Dec 29

Overall, 29.8% of the patients stopped taking their medication.
The major factors leading to non-compliance included patients beginning to feel better (45.1% and 38.6%), lack of knowledge on the benefits of completing a course (25.7%), running out of drugs at home (25.4%) and TB drugs too strong (20.1% and 20.2%).
This study established that 29.8% of TB patients failed to comply with TB drug taking regimen once they started feeling better.



Drugs used
Rifampicin
A semisynthetic antibiotic produced from Streptomyces mediterranei. It has a broad antibacterial spectrum, including activity against several forms of Mycobacterium. In susceptible organisms it inhibits DNA-dependent RNA polymerase activity by forming a stable complex with the enzyme. It thus suppresses the initiation of RNA synthesis. Rifampin is bactericidal, and acts on both intracellular and extracellular organisms.
Ethambutol
An antitubercular agent that inhibits the transfer of mycolic acids into the cell wall of the tubercle bacillus. It may also inhibit the synthesis of spermidine in mycobacteria. The action is usually bactericidal, and the drug can penetrate human cell membranes to exert its lethal effect.
Isoniazid
Antibacterial agent used primarily as a tuberculostatic. It remains the treatment of choice for tuberculosis
Pyrazinamide
A pyrazine that is used therapeutically as an antitubercular agent
Direct Observation Therapy Treatment
Patrick K Moonan, Teresa N Quitugua, and Stephen E Weis, Does directly observed therapy (DOT) reduce drug resistant tuberculosis?, BMC Public Health 2011, 11:19
Isolates from 1,706 persons collected during 1,721 episodes of tuberculosis were genotyped. Cluster members from the selective DOT county were more than twice as likely than cluster members from the universal DOT county to have at least one isolate resistant to isoniazid, rifampin, and/or ethambutol (OR = 2.3, 95% CI: 1.7, 3.1). Selective DOT county isolates were nearly 5 times more likely than universal DOT county isolates to belong to clusters with at least 2 resistant isolates having identical resistance patterns (OR = 4.7, 95% CI: 2.9, 7.6).
Conclusions:
Universal DOT for tuberculosis is associated with a decrease in the acquisition and transmission of resistant tuberculosis.

The Lancet, Volume 373, Issue 9657, Pages 15 - 16, 3 January 2009
doi:10.1016/S0140-6736(08)61938-8
Mobile Phones worldwide:
South Africa has proven a fertile testing ground for new drug-compliance technologies. According to WHO, South Africa had a 71% DOTS treatment success rate in 2005; most patients who were not successfully treated under DOTS defaulted on their treatment.
Several companies have addressed the compliance problem by developing devices that remind patients to take their medication, and feature back-up links to health workers, friends, or family members if the patient fails to respond to the first reminder.
Although tuberculosis is a disease affecting poor people, even those living on US$1 per day increasingly have access to mobile phones. There are more than 3·3 billion mobile-phone subscriptions worldwide. By the end of 2006, according to the International Telecommunications Union, 68% of those subscriptions were in developing countries.
Previous Detection of Tongue Color
Qi Zhang, Hui-Liang Shang*, Jia-jun Zhu, Min-Min Jin, Wen-Xin Wang, Qing-sheng Kong, A new tongue diagnosis application on Android platform, Bioinformatics and Biomedicine (BIBM), 2013 IEEE International Conference on, p334 – 327, 18-21 Dec. 2013.
R. Kanawong, T. Obafemi-Ajayi, T. Ma, D. Xu, S. Li, and Y. Duan, “Automated tongue feature extraction for ZHENG classification in traditional Chinese medicine,” Evidence-Based Complementary and Alternative Medicine, vol. 2012, Article ID 912852, 14 pages, 2012
Face Detection
Aamer S.S.Mohamed, Ying Weng, Stan S Ipson, and Jianmin Jiang, "Face Detection based on Skin Color in Image by Neural Networks", International Conference on Intelligence and Advance Systems, KL Convention Center, Kuala Lumpur, Malaysia, 2007
Knowledge based method
Image based method
Feature based method
Template matching method

Survey of Existing Methods
1. Education and counseling to the patient (verbal, email, SMS, interactive websites and mobile apps)

2. Financial incentives and reimbursements
Cash Incentive (immediate or deferred)
Vouchers (e.g. for groceries etc.) / Provisions (e.g. for travel) / Toys

3. Negative Reinforcement (e.g. Students barred from school for non-adherence)

4. MediSafe project
5. SIMMed

6. SimPill

7. Optical Sensors

8. MagneTrace

9. Operation Asha [DOTS]

10. MDOTS

11. X Out TB

Our method
Wet Lab Experiments
Survey Of Colors:
Blue No. 1
Blue No. 2
Green No. 3
Yellow No. 6
Red No. 40
Red No. 3
Yellow No. 5
Comparison of Color on dilution with water
Analysing Range of pH for Tongue
The mean pH (+/-s.d.) of all sites was 6.681 +/- 0.4621 with significant differences between mean pH values in the palate (7.265 +/- 0.26), the floor of the mouth (6.398 +/- 0.21), the buccal mucosa (6.204 +/- 0.179) and the tongue (6.856 +/- 0.166). No significant correlation was found between age, gender and pH at tongue sites. This could be further analyzed with more data.

Each color was thus tested at a pH of 6.856. It was observed that purple changes to yellow at that pH. Green and orange start to disappear at that pH while Blue 1, Blue 2 and Red maintain their colour.
Spectrophotometric Analysis
Adherence
and
Discoloration Period
Dry Lab Work
Face Detection
FaceDetector.Face class

Confidence that it’s actually a face – a float value between 0 and 1.
Distance between the eyes – in pixels.
Position (x, y) of the mid-point between the eyes.
Pose rotations (X, Y, Z).
Tongue Detection
Conclusion
Ways to cheat
Extensive testing database
Implementation of camera
Future prospects
[1] Aamer S.S.Mohamed, Ying Weng, Stan S Ipson, and Jianmin Jiang, "Face Detection based on Skin Color in Image by Neural Networks", International Conference on Intelligence and Advance Systems, KL Convention Center, Kuala Lumpur, Malaysia, 2007
[2] Addington WW. Patient compliance: the most serious remaining problem in the control of tuberculosis in the United States. Chest 1979; 76:741.
[3] Centre for health market innovations, funded by the Department for International Development, the Bill & Melinda Gates Foundation and Rocketfeller Foundation; URL: http://healthmarketinnovations.org/program/operation-asha
[4] Danya International, Inc., Pilot Study Using Video Cell Phones for Mobile Direct Observation Treatment (MDOT) to Monitor Medication Compliance of TB Patients,
SILVER SPRING, Md., March 23 /PRNewswire
[5] Devices and methods for monitoring drug therapy compliance patent US 6663846 B1; U.S. Provisional Application Serial No. 60/113,116 filed Dec. 21, 1998
[6] Eliza Barclay, Text messages could hasten tuberculosis drug compliance, The Lancet, Volume 373, Issue 9657, Pages 15 - 16, 3 January 2009
[7] FDA list, Color Additives Approved for Use in Drugs, Part 74, Subpart B: Color additives subject to batch certification
[8] Frederick AD Kaona1*, Mary Tuba1, Seter Siziya2 and Lenganji Sikaona3, An assessment of factors contributing to treatment adherence and knowledge of TB transmission among patients on TB treatment, BMC Public Health. 2004; 4: 68, Published online Dec 29.
[9] From Gilman et al., Goodman and Gilman's The Pharmacological Basis of Therapeutics, 9th ed, p1160

[10] From Smith and Reynard, Textbook of Pharmacology, 1992, p863

[11] Grand Challenges in TB Control Program Overview

[12] Gérard Reach, Can Technology Improve Adherence to Long-Term Therapies?,J Diabetes Sci Technol. 2009 May; 3(3): 492–499. Published online 2009 May.

[13] Global Tuberculosis Report, Published by WHO 2013, Executive summary

[14] Global Tuberculosis Report, Published by WHO 2013, Chapter 1, Box 1.1
[15] Guidelines for the Programmatic Management of Drug-resistant Tuberculosis, chapter-1; 2-7

[16] Jill Morton, The Color of Medications: Taking the Color of Medications Seriously, Science News 2004.

[17] Marisa Torrieri, Patient compliance: technology tools for physicians, Physicians Practice, September 2012.

[18] Mason JO. Opportunities for the elimination of tuberculosis. Am Rev Respir Dis 1986; 134:201.
[19] Online drug bank : www.drugbank.ca
[20] Pablos-Méndez A, Knirsch CA, Barr RG, et al. Nonadherence in tuberculosis treatment: predictors and consequences in New York City. Am J Med 1997; 102:164.
[21] Patrick K Moonan, Teresa N Quitugua, and Stephen E Weis, Does directly observed therapy (DOT) reduce drug resistant tuberculosis?, BMC Public Health 2011, 11:19

[22] Qi Zhang, Hui-Liang Shang*, Jia-jun Zhu, Min-Min Jin, Wen-Xin Wang, Qing-sheng Kong, A new tongue diagnosis application on Android platform, Bioinformatics and Biomedicine (BIBM), 2013 IEEE International Conference on, p334 – 327, 18-21 Dec. 2013.

[23] R. Kanawong, T. Obafemi-Ajayi, T. Ma, D. Xu, S. Li, and Y. Duan, “Automated tongue feature extraction for ZHENG classification in traditional Chinese medicine,” Evidence-Based Complementary and Alternative Medicine, vol. 2012, Article ID 912852, 14 pages, 2012

[24] Research going on at GaTech got from http://www.gtresearchnews.gatech.edu/sensor-necklace/

[25] Roam Khatko(Intel), Implementing Face Detection in Android,10/28/2013 - 11:33
[26] Sujay R, George Mathew, Health care through Community Participation: Role of ASHAs, Economic and Political Weekly 47, 10, 70-76, March 2012.
[27]Stephen K. Field, Dina Fisher, New treatment options for multidrug-resistant tuberculosis, Ther Adv Resp Dis. 2012;6(5):255-268.

[28] The International Journal of Tuberculosis and Lung Disease, Volume 2, Number 1, January 1998 , pp. 10-15(6)

[29] Weis, Stephen E.; Slocum, Philip C.; Blais, Francis X.; King, Barbara; Nunn, Mary; Matney, G. Burgis; Gomez, Enriqueta; Foresman, Brian H, The effect of directly observed therapy on the rates of drug resistance and relapse in tuberculosis, The New England Journal of Medicine, Vol 330(17), Apr 1994, 1179-1184.

[30] Images got from WHO website. Url : http://gamapserver.who.int/mapLibrary/app/searchResults.aspx
Literature Survey
Thankyou
Android Development
ADT v.22.3
Eclipse
JDK 6
Initial App
<?xml version="1.0" encoding="UTF-8"?>

-<manifest android:versionName="1.0" android:versionCode="1" package="com.example.cameracap" xmlns:android="http://schemas.android.com/apk/res/android">

<uses-permission android:name="android.permission.CAMERA"/>
<uses-feature android:name="android.hardware.camera"/>
<uses-feature android:name="android.hardware.camera.autofocus"/>

-<application android:theme="@style/AppTheme" android:label="@string/app_name" android:icon="@drawable/ic_launcher" android:allowBackup="true">

-<activity android:name="com.example.cameracap.MainActivity" android:label="@string/app_name">

-<intent-filter>
<action android:name="android.intent.action.MAIN"/>
<category android:name="android.intent.category.LAUNCHER"/>
</intent-filter>
</activity>
</application>
</manifest>
Android Manifests
import java.io.ByteArrayOutputStream;
import android.graphics.Canvas;
import java.io.FileNotFoundException;
import java.io.IOException;
import java.io.InputStream;
import android.app.Activity;
import android.content.ActivityNotFoundException;
import android.content.Intent;
import android.graphics.Bitmap;
import android.graphics.BitmapFactory;
import android.graphics.Canvas;
import android.graphics.Color;
import android.graphics.Paint;
import android.graphics.PointF;
import android.media.FaceDetector;
import android.media.FaceDetector.Face;
import android.net.Uri;
import android.os.Bundle;
import android.provider.MediaStore;
import android.view.Menu;
import android.view.View;
import android.view.View.OnClickListener;
import android.widget.Button;
import android.widget.ImageView;
import android.widget.Toast
@Override
protected void onCreate(Bundle savedInstanceState)
{
super.onCreate(savedInstanceState);
setContentView(R.layout.activity_main);

//retrieve a reference to the UI button
Button captureBtn = (Button)findViewById(R.id.capture_btn);

//handle button clicks
captureBtn.setOnClickListener(this);
}
Main Activity Class
public void onClick(View v) {
if (v.getId() == R.id.capture_btn)
{
try {
Toast toastie = Toast.makeText(this,"it's starting :)",Toast.LENGTH_SHORT);
toastie.show();

Intent captureIntent = new Intent(MediaStore.ACTION_IMAGE_CAPTURE);

//handle the returned data in onActivityResult
startActivityForResult(captureIntent, CAMERA_CAPTURE);
}
catch(ActivityNotFoundException anfe)
{
//display an error message
String errorMessage = "Whoops - your phone does not support camera!";
Toast toastie = Toast.makeText(this, errorMessage, Toast.LENGTH_SHORT);
toastie.show();
}}
protected void onActivityResult(int requestCode, int resultCode, Intent data) {
if (resultCode == RESULT_OK)
{
//user is returning from capturing an image using the camera
if(requestCode == CAMERA_CAPTURE)
{
//get the Uri for the captured image
Toast toaster = Toast.makeText(this,"part2",Toast.LENGTH_SHORT);
toaster.show();
picUri = data.getData();
ImageView picture = (ImageView)findViewById(R.id.picture);

//convert to bitmap and display in image view
Bundle extras = data.getExtras();
thePic = extras.getParcelable("data");
picture.setImageBitmap(thePic);
performAnalysis();
}
private void performAnalysis()
{
int pich = thePic.getHeight();
int picw = thePic.getWidth();

int[] pix = new int[picw * pich];
int blue_pix_counter = 0;

thePic.getPixels(pix, 0, picw, 0, 0, picw, pich);
int p = 0,b=0;
for (int i = 0; i<picw; i++){
for (int j=0; j<pich; j++){
p = thePic.getPixel(i,j);
b = Color.blue(p);
if (b > 200)
blue_pix_counter++;} }

toastie = Toast.makeText(this,Integer.toString(blue_pix_counter), Toast.LENGTH_SHORT);
toastie.show();
}
<?xml version="1.0"?>

-<manifest android:versionName="1.0" android:versionCode="1" package="com.example.facedetectionexample" xmlns:android="http://schemas.android.com/apk/res/android">

<uses-sdk android:targetSdkVersion="15" android:minSdkVersion="8"/>
<uses-permission android:name="android.permission.WRITE_EXTERNAL_STORAGE"/>
-<application android:theme="@style/AppTheme" android:label="@string/app_name" android:icon="@drawable/ic_launcher">

-<activity android:name=".FaceDetectionExample" android:label="@string/title_activity_face_detection_example">
-<intent-filter>
<action android:name="android.intent.action.MAIN"/>
<category android:name="android.intent.category.LAUNCHER"/>
</intent-filter>
</activity>
</application>
</manifest>
Android Manifests
import android.app.Activity;
import android.content.Context;
import android.graphics.Bitmap;
import android.graphics.BitmapFactory;
import android.graphics.Canvas;
import android.graphics.Color;
import android.graphics.Paint;
import android.graphics.PointF;
import android.graphics.Region;
import android.media.FaceDetector;
import android.media.FaceDetector.Face;
import android.os.Bundle;
import android.view.View;
import android.widget.Toast;
private class myView extends View {

private int imageWidth, imageHeight;
private int numberOfFace = 7;
private FaceDetector myFaceDetect;
private FaceDetector.Face[] myFace;
float myEyesDistance;
int numberOfFaceDetected;

Bitmap myBitmap;

public myView(Context context) {
super(context);

BitmapFactory.Options BitmapFactoryOptionsbfo = new BitmapFactory.Options();
BitmapFactoryOptionsbfo.inPreferredConfig = Bitmap.Config.RGB_565;

myBitmap = BitmapFactory.decodeResource(getResources(),R.drawable.bluetongue1, BitmapFactoryOptionsbfo);

imageWidth = myBitmap.getWidth();
imageHeight = myBitmap.getHeight();

myFace = new FaceDetector.Face[numberOfFace];
myFaceDetect = new FaceDetector(imageWidth, imageHeight,numberOfFace);

numberOfFaceDetected = myFaceDetect.findFaces(myBitmap, myFace);

Toast toastie = Toast.makeText(getApplicationContext(),Integer.toString(numberOfFaceDetected), Toast.LENGTH_SHORT);
toastie.show();
}
protected void onDraw(Canvas canvas) {
canvas.drawBitmap(myBitmap, 0, 0, null);

Paint myPaint = new Paint();
myPaint.setColor(Color.GREEN);
myPaint.setStyle(Paint.Style.STROKE);
myPaint.setStrokeWidth(3);

for (int i = 0; i < numberOfFaceDetected; i++) {
Face face = myFace[i];
PointF myMidPoint = new PointF();
face.getMidPoint(myMidPoint);
myEyesDistance = face.eyesDistance();


canvas.drawRect((int) (myMidPoint.x - myEyesDistance * 2),
(int) (myMidPoint.y - myEyesDistance * 2),
(int) (myMidPoint.x + myEyesDistance * 2),
(int) (myMidPoint.y + myEyesDistance * 2), myPaint);


myPaint.setColor(Color.RED);

canvas.drawRect( (int) (myMidPoint.x - myEyesDistance * 0.7),
(int) (myMidPoint.y + myEyesDistance * 0.5),
(int) (myMidPoint.x + myEyesDistance * 0.7),
(int) (myMidPoint.y + myEyesDistance * 1.75), myPaint);

myPaint.setColor(Color.GREEN);
cropImage = Bitmap.createBitmap(myBitmap,(int) (myMidPoint.x - myEyesDistance * 0.7),
(int) (myMidPoint.y + myEyesDistance * 0.5),
125,125);
//canvas.drawColor(Color.WHITE);
canvas.drawBitmap(cropImage, 0, 0, null);

int pich = cropImage.getHeight();
int picw = cropImage.getWidth();

int[] pix = new int[picw * pich];
int blue_pix_counter = 0;

cropImage.getPixels(pix, 0, picw, 0, 0, picw, pich);
int p = 0;
int r,g,b,y;
for (i = 0; i<picw; i++){
for (int j=0; j<pich; j++){
p = cropImage.getPixel(i,j);
b = Color.blue(p);
if (
b > 200
)
blue_pix_counter++;}}

Toast toastie = Toast.makeText(getApplicationContext(),Integer.toString(blue_pix_counter), Toast.LENGTH_SHORT);
toastie.show();

if(
blue_pix_counter > 80
){
toastie = Toast.makeText(getApplicationContext(),"
Pill taken
",Toast.LENGTH_SHORT);
}
else{
toastie = Toast.makeText(getApplicationContext(),"
Pill not taken
",Toast.LENGTH_SHORT);
}
toastie.show();
Full transcript