Corrected diagnosis

app.py

@@ -173,7 +173,7 @@ def get_diagnosis(brain_img, input_name, input_age, input_phone_num, input_emer_
     
     # Probabilidad de tener MCI
     print(prob)
-    diagnosis = f"The patient has a probability of {(100*prob):.2f}% of having MCI"
+    diagnosis = f"The patient has a probability of {(100*prob):.2f}% of having MCI with a sensitivity of 92.00% and a specificity of 92.75%"
     
     save_file(input_name, input_age, input_phone_num, input_emer_name, input_emer_phone_num,
                   input_sex, input_MMSE, input_GDSCALE, input_CDR, input_FAQ, input_NPI_Q, input_Diastolic_blood_pressure,
@@ -191,94 +191,121 @@ def clear():
 '''--------------------------------- Interfaz ---------------------------------'''
 
 with gr.Blocks(theme=gr.themes.Base()) as demo:
+    
     with gr.Row():
         # gr.HTML(r"""<center><img src='https://user-images.githubusercontent.com/66338785/233529518-33e8bcdb-146f-49e8-94c4-27d6529ce4f7.png' width="30%" height="30%"></center>""")
-        gr.HTML(r"""<center><img src='https://user-images.githubusercontent.com/66338785/233531457-f368e04b-5099-42a8-906d-6f1250ea0f1e.png' width="40%" height="40%"></center>""")
-        # gr.Markdown("""
-        #             # SIMCI
-        #             Interfaz de SIMCI
-        #             """)
+        gr.HTML(r"""
+                <center><img src='https://user-images.githubusercontent.com/66338785/233531457-f368e04b-5099-42a8-906d-6f1250ea0f1e.png' width="40%" height="40%"></center>
+                """)
     
     # Inputs
     with gr.Row():
         with gr.Column(variant="panel", scale=1):
-            gr.Markdown('<h2 style="text-align: center; color:#235784;">Patient Information</h2>')
-            with gr.Tab("Personal data"):
-                # Objeto para subir archivo nifti
-                input_name = gr.Textbox(placeholder='Enter the patient name', label='Patient name')
-                input_age = gr.Number(label='Age')
-                input_phone_num = gr.Number(label='Phone number')
-                input_emer_name = gr.Textbox(placeholder='Enter the emergency contact name', label='Emergency contact name')
-                input_emer_phone_num = gr.Number(label='Emergency contact name phone number')
-                input_sex = gr.Dropdown(["Male", "Female"], label="Sex")
+            with gr.Tab("Patient Information"):  
+                gr.Markdown('<h2 style="text-align: center; color:#235784;">Patient Information</h2>')
+                with gr.Tab("Personal data"):
+                    # Objeto para subir archivo nifti
+                    input_name = gr.Textbox(placeholder='Enter the patient name', label='Patient name')
+                    input_age = gr.Number(label='Age', value=None)
+                    input_phone_num = gr.Number(label='Phone number')
+                    input_emer_name = gr.Textbox(placeholder='Enter the emergency contact name', label='Emergency contact name')
+                    input_emer_phone_num = gr.Number(label='Emergency contact name phone number', value=None)
+                    input_sex = gr.Dropdown(["Male", "Female"], label="Sex")
+                    
+                with gr.Tab("Clinical data"):
+                    input_MMSE = gr.Slider(minimum=0,
+                                           maximum=30,
+                                           value=0,
+                                           step=1,
+                                           label="MMSE total score") 
+                    
+                    input_GDSCALE = gr.Slider(minimum=0,
+                                           maximum=12,
+                                           value=0,
+                                           step=1,
+                                           label="GDSCALE total score") 
+                    
+                    input_CDR = gr.Slider(minimum=0,
+                                           maximum=3,
+                                           value=0,
+                                           step=0.5,
+                                           label="Global CDR") 
+                    
+                    input_FAQ = gr.Slider(minimum=0,
+                                           maximum=30,
+                                           value=0,
+                                           step=1,
+                                           label="FAQ total score") 
+                    
+                    input_NPI_Q =  gr.Slider(minimum=0,
+                                           maximum=30,
+                                           value=0,
+                                           step=1,
+                                           label="NPI-Q total score") 
+                          
+                    
+                with gr.Tab("Vital Signs"):
+                    input_Diastolic_blood_pressure = gr.Number(label='Diastolic Blood Pressure(mm Hg)')
+                    input_Systolic_blood_pressure = gr.Number(label='Systolic Blood Pressure(mm Hg)')
+                    input_Body_heigth = gr.Number(label='Body heigth (cm)')
+                    input_Body_weight = gr.Number(label='Body weigth (kg)')
+                    input_Heart_rate = gr.Number(label='Heart rate (bpm)')
+                    input_Respiratory_rate = gr.Number(label='Respiratory rate (bpm)')
+                    input_Body_temperature = gr.Number(label='Body temperature (°C)')
+                    input_Pluse_oximetry = gr.Number(label='Pluse oximetry (%)')
+                    
+                with gr.Tab("Medications"):
+                    input_medications = gr.Textbox(label='Medications', lines=5)
+                    input_allergies = gr.Textbox(label='Allergies', lines=5)
+                    
+                with gr.Box():
+                    gr.Markdown('<h4 style="color:#235784;">Upload MRI</h4>')             
+                    input_file = gr.File(file_count="single", label="MRI File (.nii)", file_types=[".nii"], show_label=False)
                 
-            with gr.Tab("Clinical data"):
-                input_MMSE = gr.Slider(minimum=0,
-                                       maximum=30,
-                                       value=0,
-                                       step=1,
-                                       label="MMSE total score") 
+                with gr.Row():
+                    # Botón para cargar imagen
+                    load_img_button = gr.Button(value="Load")
+                    
+                    # Botón para borrar
+                    clear_button = gr.Button(value="Clear")
                 
-                input_GDSCALE = gr.Slider(minimum=0,
-                                       maximum=12,
-                                       value=0,
-                                       step=1,
-                                       label="GDSCALE total score") 
+                # Botón para procesar imagen
+                process_button = gr.Button(value="Process MRI", visible=False, variant="primary")
                 
-                input_CDR = gr.Slider(minimum=0,
-                                       maximum=3,
-                                       value=0,
-                                       step=0.5,
-                                       label="Global CDR") 
+                # Botón para obtener diagnostico
+                diagnostic_button = gr.Button(value="Get diagnosis", visible=False, variant="primary")
                 
-                input_FAQ = gr.Slider(minimum=0,
-                                       maximum=30,
-                                       value=0,
-                                       step=1,
-                                       label="FAQ total score") 
+                with gr.Box(visible=False) as download_box:
+                    gr.Markdown('<h4 style="color:#235784;"> Download diagnosis report</h4>')  
+                    # Descarga de archivo
+                    output_file = gr.File(file_count="single", show_label=False, interactive=False, visible=True)
                 
-                input_NPI_Q =  gr.Slider(minimum=0,
-                                       maximum=30,
-                                       value=0,
-                                       step=1,
-                                       label="NPI-Q total score") 
-                      
+            with gr.Tab("About"):  
+                gr.Markdown('''
+                # SIMCI
+                Alzheimer is a degenerative and irreversible neurological disorder that affects cognitive abilities and daily activities. It is divided into three stages: preclinical, mild cognitive impairment (MCI), and dementia. MCI represents a transition to dementia, characterized by a greater-than-expected decline in cognitive function without interference in daily activities. It is estimated that approximately 20% of individuals with MCI progress to dementia, but diagnostic errors are common in this early stage due to ambiguous cognitive changes, including those observed in neuroimaging.
                 
-            with gr.Tab("Vital Signs"):
-                input_Diastolic_blood_pressure = gr.Number(label='Diastolic Blood Pressure(mm Hg)')
-                input_Systolic_blood_pressure = gr.Number(label='Systolic Blood Pressure(mm Hg)')
-                input_Body_heigth = gr.Number(label='Body heigth (cm)')
-                input_Body_weight = gr.Number(label='Body weigth (kg)')
-                input_Heart_rate = gr.Number(label='Heart rate (bpm)')
-                input_Respiratory_rate = gr.Number(label='Respiratory rate (bpm)')
-                input_Body_temperature = gr.Number(label='Body temperature (°C)')
-                input_Pluse_oximetry = gr.Number(label='Pluse oximetry (%)')
+                SIMCI is a system for detecting mild cognitive impairment that employs a multimodal approach and a stratification process to address this issue. The system utilizes demographic characteristics and clinical test results to provide medical interpretability and assist specialists in decision-making. The database includes magnetic resonance imaging (MRI) brain scans, clinical examination results, and demographic information. SIMCI achieves an F1-score of 0.9233, a sensitivity of 0.9200, and a specificity of 0.9275.
                 
-            with gr.Tab("Medications"):
-                input_medications = gr.Textbox(label='Medications', lines=5)
-                input_allergies = gr.Textbox(label='Allergies', lines=5)
+                <center><img src='https://github.com/SebastianBravo/Battery_monitoring_system/assets/66338785/64cd8821-8249-4020-a00d-a6ae5a7a7e53' width="80%" height="80%"></center>
                 
-            with gr.Box():
-                gr.Markdown('<h4 style="color:#235784;">Upload MRI</h4>')             
-                input_file = gr.File(file_count="single", label="MRI File (.nii)", file_types=[".nii"], show_label=False)
-            
-            with gr.Row():
-                # Botón para cargar imagen
-                load_img_button = gr.Button(value="Load")
+                ## Repository 
+                https://github.com/SebastianBravo/SIMCI
                 
-                # Botón para borrar
-                clear_button = gr.Button(value="Clear")
-            
-            # Botón para procesar imagen
-            process_button = gr.Button(value="Process MRI", visible=False, variant="primary")
-            
-            # Botón para obtener diagnostico
-            diagnostic_button = gr.Button(value="Get diagnosis", visible=False, variant="primary")
-            
-            with gr.Box(visible=False) as download_box:
-                gr.Markdown('<h4 style="color:#235784;"> Download diagnosis report</h4>')  
-                # Descarga de archivo
-                output_file = gr.File(file_count="single", show_label=False, interactive=False, visible=True)
+                ## Authors
+                
+                 - Daniel Stiven Zambrano Acosta <span>B.Sc</span>
+                 - Juan Sebastián Bravo Santacruz <span>B.Sc</span>
+                 - Wilson Javier Arenas López <span>M.Sc</span>
+                 - Pablo Alexander Reyes Gavilan PhD
+                 - Miguel Alfonso Altuve PhD
+                
+                
+                ## Acknowledgement
+                
+                 - Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.
+                 - We thank [MedicalNet](https://github.com/Tencent/MedicalNet) and [segmentation_models_3D](https://github.com/ZFTurbo/segmentation_models_3D) which we build SIMCI.
+                ''')
     
         # Outputs 
         with gr.Column(variant="panel", scale=1):
@@ -317,7 +344,7 @@ with gr.Blocks(theme=gr.themes.Base()) as demo:
                 # Texto del diagnostico
                 diagnosis_text = gr.Textbox(label="Diagnosis",interactive=False, placeholder="The diagnosis will show here...")
     
-    # componentes = 
+    
     
     # Variables
     original_input_sitk = gr.State()