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# Install required packages
#!pip install gradio tensorflow opencv-python scikit-learn
import gradio as gr
import tensorflow as tf
import numpy as np
import cv2
from tensorflow.keras.applications import ResNet50
from tensorflow.keras.applications.resnet50 import preprocess_input
from tensorflow.keras.preprocessing.image import img_to_array
from sklearn.preprocessing import StandardScaler
import pandas as pd
class SurgicalAssistSystem:
def __init__(self):
# Initialize the image processing model (ResNet50 pretrained)
self.image_model = ResNet50(weights='imagenet', include_top=False,
input_shape=(224, 224, 3))
# Initialize scaler for vital signs
self.scaler = StandardScaler()
# Define normal ranges for vital signs
self.vital_ranges = {
'heart_rate': (60, 100),
'blood_pressure_systolic': (90, 140),
'blood_pressure_diastolic': (60, 90),
'oxygen_saturation': (95, 100),
'temperature': (36.5, 37.5)
}
def preprocess_image(self, image):
# Resize image to expected dimensions
image = cv2.resize(image, (224, 224))
image = img_to_array(image)
image = np.expand_dims(image, axis=0)
image = preprocess_input(image)
return image
def analyze_image(self, image):
# Preprocess and analyze the image
processed_image = self.preprocess_image(image)
features = self.image_model.predict(processed_image)
# Simplified analysis - detecting potential anomalies
feature_mean = np.mean(features)
if feature_mean > 0.5:
return "Potential anomaly detected in the surgical field"
else:
return "No immediate concerns in the surgical field"
def analyze_vitals(self, vitals_dict):
alerts = []
for vital, value in vitals_dict.items():
if vital in self.vital_ranges:
min_val, max_val = self.vital_ranges[vital]
if value < min_val:
alerts.append(f"Warning: {vital} is below normal range")
elif value > max_val:
alerts.append(f"Warning: {vital} is above normal range")
if not alerts:
return "All vital signs are within normal ranges"
return "\n".join(alerts)
def generate_recommendations(self, image_analysis, vitals_analysis):
recommendations = []
if "anomaly" in image_analysis.lower():
recommendations.append("- Recommend detailed inspection of highlighted area")
recommendations.append("- Consider additional imaging")
if "warning" in vitals_analysis.lower():
recommendations.append("- Monitor vital signs closely")
recommendations.append("- Consider adjusting procedure parameters")
if not recommendations:
recommendations.append("- Proceed with standard protocol")
recommendations.append("- Continue routine monitoring")
return "\n".join(recommendations)
# Initialize the system
system = SurgicalAssistSystem()
def process_surgical_data(image, heart_rate, blood_pressure_systolic,
blood_pressure_diastolic, oxygen_saturation, temperature):
# Convert image to numpy array if it's not already
if isinstance(image, str):
return "Please provide a valid image"
# Create vitals dictionary
vitals = {
'heart_rate': heart_rate,
'blood_pressure_systolic': blood_pressure_systolic,
'blood_pressure_diastolic': blood_pressure_diastolic,
'oxygen_saturation': oxygen_saturation,
'temperature': temperature
}
# Analyze image and vitals
image_analysis = system.analyze_image(image)
vitals_analysis = system.analyze_vitals(vitals)
recommendations = system.generate_recommendations(image_analysis, vitals_analysis)
return (f"Image Analysis:\n{image_analysis}\n\n"
f"Vitals Analysis:\n{vitals_analysis}\n\n"
f"Recommendations:\n{recommendations}")
# Create Gradio interface
iface = gr.Interface(
fn=process_surgical_data,
inputs=[
gr.Image(label="Surgical Field Image"),
gr.Number(label="Heart Rate (bpm)"),
gr.Number(label="Blood Pressure - Systolic (mmHg)"),
gr.Number(label="Blood Pressure - Diastolic (mmHg)"),
gr.Number(label="Oxygen Saturation (%)"),
gr.Number(label="Temperature (°C)")
],
outputs=gr.Textbox(label="Analysis Results"),
title="Surgical Assistance System",
description="Upload an image of the surgical field and enter vital signs for analysis and recommendations."
)
# Launch the interface
iface.launch(share=True) |