ECG2AF open-source weights and notebook

docker/vm_boot_images/config/tensorflow-requirements.txt

@@ -44,4 +44,3 @@ umap-learn[plot]
 neurite
 voxelmorph
 pystrum
-

ml4h/TensorMap.py

@@ -204,8 +204,6 @@ def __init__(
         elif self.activation is None and (self.is_survival_curve() or self.is_time_to_event()):
             self.activation = 'sigmoid'
 
-
-
         if self.channel_map is None and self.is_time_to_event():
             self.channel_map = DEFAULT_TIME_TO_EVENT_CHANNELS
 

ml4h/data_descriptions.py

@@ -5,6 +5,7 @@
 from typing import Callable, List, Union, Optional, Tuple, Dict, Any
 
 import h5py
+import datetime
 import numcodecs
 import numpy as np
 import pandas as pd
@@ -331,10 +332,9 @@ def __init__(
     ):
         """
         Gets data from a column of the provided DataFrame.
-        :param df: Must be multi-indexed with sample_id, loading_option
-        # TODO: allow multiple loading options
         :param col: The column name to get data from
         :param process_col: Function to turn the column value into Tensor
+        :param name: Optional overwrite of the df column name
         """
         self.process_col = process_col or self._default_process_call
         self.df = df

ml4h/models/legacy_models.py

@@ -350,7 +350,7 @@ def make_hidden_layer_model(parent_model: Model, tensor_maps_in: List[TensorMap]
     dummy_input = {tm.input_name(): np.zeros((1,) + parent_model.get_layer(tm.input_name()).input_shape[0][1:]) for tm in tensor_maps_in}
     intermediate_layer_model = Model(inputs=parent_inputs, outputs=target_layer.output)
     # If we do not predict here then the graph is disconnected, I do not know why?!
-    intermediate_layer_model.predict(dummy_input)
+    intermediate_layer_model.predict(dummy_input, verbose=0)
     return intermediate_layer_model
 
 
@@ -1344,7 +1344,7 @@ def make_paired_autoencoder_model(
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 def embed_model_predict(model, tensor_maps_in, embed_layer, test_data, batch_size):
     embed_model = make_hidden_layer_model(model, tensor_maps_in, embed_layer)
-    return embed_model.predict(test_data, batch_size=batch_size)
+    return embed_model.predict(test_data, batch_size=batch_size, verbose=0)
 
 
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

ml4h/plots.py

@@ -770,7 +770,7 @@ def plot_scatter(
 
     ax1.set_xlabel("Predictions")
     ax1.set_ylabel("Actual")
-    ax1.set_title(title)
+    ax1.set_title(f'{title} N = {len(prediction)}' )
     ax1.legend(loc="lower right")
 
     sns.distplot(prediction, label="Predicted", color="r", ax=ax2)
@@ -2253,7 +2253,7 @@ def plot_ecg_rest(
     tensor_paths: List[str],
     rows: List[int],
     out_folder: str,
-    is_blind: bool,
+    is_blind: bool
 ) -> None:
     """Plots resting ECGs including annotations and LVH criteria
 

ml4h/tensormap/mgb/xdl.py

@@ -0,0 +1,45 @@
+from typing import Dict
+
+import h5py
+import numpy as np
+from ml4h.TensorMap import TensorMap, Interpretation
+
+ecg_5000_std = TensorMap('ecg_5000_std', Interpretation.CONTINUOUS, shape=(5000, 12))
+
+hypertension_icd_only = TensorMap(name='hypertension_icd_only', interpretation=Interpretation.CATEGORICAL,
+                                  channel_map={'no_hypertension_icd_only': 0, 'hypertension_icd_only': 1})
+hypertension_icd_bp = TensorMap(name='hypertension_icd_bp', interpretation=Interpretation.CATEGORICAL,
+                                channel_map={'no_hypertension_icd_bp': 0, 'hypertension_icd_bp': 1})
+hypertension_icd_bp_med = TensorMap(name='hypertension_icd_bp_med', interpretation=Interpretation.CATEGORICAL,
+                                    channel_map={'no_hypertension_icd_bp_med': 0, 'hypertension_icd_bp_med': 1})
+hypertension_med = TensorMap(name='start_fu_hypertension_med', interpretation=Interpretation.CATEGORICAL,
+                             channel_map={'no_hypertension_medication': 0, 'hypertension_medication': 1})
+
+lvef = TensorMap(name='LVEF', interpretation=Interpretation.CONTINUOUS, channel_map={'LVEF': 0})
+
+age = TensorMap(name='age_in_days', interpretation=Interpretation.CONTINUOUS, channel_map={'age_in_days': 0})
+sex = TensorMap(name='sex', interpretation=Interpretation.CATEGORICAL, channel_map={'Female': 0, 'Male': 1})
+
+cad = TensorMap(name='cad', interpretation=Interpretation.CATEGORICAL, channel_map={'no_cad': 0, 'cad': 1})
+dm = TensorMap(name='dm', interpretation=Interpretation.CATEGORICAL, channel_map={'no_dm': 0, 'dm': 1})
+hypercholesterolemia = TensorMap(name='hypercholesterolemia', interpretation=Interpretation.CATEGORICAL,
+                                 channel_map={'no_hypercholesterolemia': 0, 'hypercholesterolemia': 1})
+
+
+def ecg_median_biosppy(tm: TensorMap, hd5: h5py.File, dependents: Dict = {}) -> np.ndarray:
+    tensor = np.zeros(tm.shape, dtype=np.float32)
+    for lead in tm.channel_map:
+        tensor[:, tm.channel_map[lead]] = hd5[f'{tm.path_prefix}{lead}']
+    tensor = np.nan_to_num(tensor)
+    return tensor
+
+ecg_channel_map = {
+    'I': 0, 'II': 1, 'III': 2, 'aVR': 3, 'aVL': 4, 'aVF': 5,
+    'V1': 6, 'V2': 7, 'V3': 8, 'V4': 9, 'V5': 10, 'V6': 11,
+}
+
+ecg_biosppy_median_60bpm = TensorMap(
+    'median', Interpretation.CONTINUOUS, path_prefix='median_60bpm_', shape=(600, 12),
+    tensor_from_file=ecg_median_biosppy,
+    channel_map=ecg_channel_map,
+)

ml4h/tensormap/ukb/demographics.py

@@ -341,10 +341,11 @@ def alcohol_from_file(tm, hd5, dependents={}):
     path_prefix='categorical', annotation_units=2,
     channel_map={'Sex_Female_0_0': 0, 'Sex_Male_0_0': 1}, loss='categorical_crossentropy',
 )
-# sex = TensorMap(
-#     'Sex_Male_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_FLAG, path_prefix='categorical', annotation_units=2,
-#     channel_map={'Sex_Female_0_0': 0, 'Sex_Male_0_0': 1}, loss='categorical_crossentropy',
-# )
+sex_dummy1 = TensorMap(
+    'sex', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_FLAG,
+    path_prefix='categorical', annotation_units=2,
+    channel_map={'Sex_Female_0_0': 0, 'Sex_Male_0_0': 1}, loss='categorical_crossentropy',
+)
 af_dummy2 = TensorMap(
     'af_in_read', Interpretation.CATEGORICAL, path_prefix='categorical', storage_type=StorageType.CATEGORICAL_FLAG,
     channel_map={'no_atrial_fibrillation': 0, 'atrial_fibrillation': 1},
@@ -354,7 +355,11 @@ def alcohol_from_file(tm, hd5, dependents={}):
      path_prefix='categorical', annotation_units=2,
     channel_map={'Sex_Female_0_0': 0, 'Sex_Male_0_0': 1}, loss='categorical_crossentropy',
 )
-
+sex_dummy3 = TensorMap(
+    'sex_from_wide', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_FLAG,
+    path_prefix='categorical', annotation_units=2,
+    channel_map={'female': 0, 'male': 1}, loss='categorical_crossentropy',
+)
 brain_volume = TensorMap(
     '25010_Volume-of-brain-greywhite-matter_2_0', Interpretation.CONTINUOUS, path_prefix='continuous', normalization={'mean': 1165940.0, 'std': 111511.0},
     channel_map={'25010_Volume-of-brain-greywhite-matter_2_0': 0}, loss='logcosh', loss_weight=0.1,

ml4h/tensormap/ukb/dxa.py

@@ -98,7 +98,7 @@ def dxa_background_erase(tm, hd5, dependents={}):
 )
 dxa_11 = TensorMap(
     'dxa_1_11',
-    shape=(896, 352, 1),
+    shape=(896, 384, 1),
     path_prefix='ukb_dxa',
     tensor_from_file=dxa_background_erase,
     normalization=ZeroMeanStd1(),

ml4h/tensormap/ukb/ecg.py

@@ -574,6 +574,7 @@ def ecg_rest_section_to_segment(tm, hd5, dependents={}):
     metrics=['mse', 'mae'], channel_map=ECG_REST_MEDIAN_LEADS, normalization=Standardize(mean=0, std=10),
 )
 
+
 ecg_rest_median_576 = TensorMap(
     'ecg_rest_median_576', Interpretation.CONTINUOUS, path_prefix='ukb_ecg_rest', shape=(576, 12), loss='logcosh',
     activation='linear', tensor_from_file=_make_ecg_rest(), channel_map=ECG_REST_MEDIAN_LEADS,
@@ -595,8 +596,10 @@ def ecg_rest_section_to_segment(tm, hd5, dependents={}):
 )
 
 ecg_rest_median_raw_10_prediction = TensorMap(
-    'ecg_rest_median_raw_10', Interpretation.CONTINUOUS, shape=(600, 12), loss='logcosh', activation='linear', normalization=ZeroMeanStd1(),
-    tensor_from_file=named_tensor_from_hd5('ecg_rest_median_raw_10_prediction'), metrics=['mse', 'mae'], channel_map=ECG_REST_MEDIAN_LEADS,
+    'ecg_rest_median_raw_10', Interpretation.CONTINUOUS, shape=(600, 12), loss='logcosh', activation='linear',
+    normalization=ZeroMeanStd1(),
+    tensor_from_file=named_tensor_from_hd5('ecg_rest_median_raw_10_prediction'), metrics=['mse', 'mae'],
+    channel_map=ECG_REST_MEDIAN_LEADS,
 )
 
 

ml4h/tensormap/ukb/mri.py

@@ -253,6 +253,25 @@ def _slice_tensor_from_file(tm, hd5, dependents={}):
     return _slice_tensor_from_file
 
 
+def _random_slice_tensor(tensor_key, max_random=50):
+    def _slice_tensor_from_file(tm, hd5, dependents={}):
+        slice_index = np.random.randint(max_random)
+        if tm.shape[-1] == 1:
+            t = pad_or_crop_array_to_shape(
+                tm.shape[:-1],
+                np.array(hd5[tensor_key][..., slice_index], dtype=np.float32),
+            )
+            tensor = np.expand_dims(t, axis=-1)
+        else:
+            tensor = pad_or_crop_array_to_shape(
+                tm.shape,
+                np.array(hd5[tensor_key][..., slice_index], dtype=np.float32),
+            )
+        return tensor
+
+    return _slice_tensor_from_file
+
+
 def _segmented_dicom_slices(dicom_key_prefix, path_prefix='ukb_cardiac_mri', step=1, total_slices=50):
     def _segmented_dicom_tensor_from_file(tm, hd5, dependents={}):
         tensor = np.zeros(tm.shape, dtype=np.float32)
@@ -389,6 +408,12 @@ def _mri_slice_blackout_tensor_from_file(tm, hd5, dependents={}):
     tensor_from_file=_slice_tensor('ukb_cardiac_mri/cine_segmented_lax_4ch/2/instance_0', 0),
 )
 
+lax_4ch_random_slice_3d = TensorMap(
+    'lax_4ch_random_slice_3d', Interpretation.CONTINUOUS, shape=(160, 224, 1),
+    normalization=ZeroMeanStd1(),
+    tensor_from_file=_random_slice_tensor('ukb_cardiac_mri/cine_segmented_lax_4ch/2/instance_0'),
+)
+
 lax_4ch_diastole_slice0_224_3d_augmented = TensorMap(
     'lax_4ch_diastole_slice0_224_3d_augmented', Interpretation.CONTINUOUS, shape=(160, 224, 1),
     normalization=ZeroMeanStd1(), augmentations=[_gaussian_noise, _make_rotate(-15, 15)],
@@ -415,6 +440,36 @@ def _mri_slice_blackout_tensor_from_file(tm, hd5, dependents={}):
         'ukb_cardiac_mri/cine_segmented_lax_2ch/2/instance_0', 0,
     ),
 )
+lax_2ch_diastole_slice_224_160_3d = TensorMap(
+    'lax_2ch_diastole_slice_224_160_3d',
+    Interpretation.CONTINUOUS,
+    shape=(224, 160, 1),
+    loss='logcosh',
+    normalization=ZeroMeanStd1(),
+    tensor_from_file=_slice_tensor(
+        'ukb_cardiac_mri/cine_segmented_lax_2ch/2/instance_0', 0,
+    ),
+)
+lax_2ch_diastole_slice_224_192_3d = TensorMap(
+    'lax_2ch_diastole_slice_224_192_3d',
+    Interpretation.CONTINUOUS,
+    shape=(224, 192, 1),
+    loss='logcosh',
+    normalization=ZeroMeanStd1(),
+    tensor_from_file=_slice_tensor(
+        'ukb_cardiac_mri/cine_segmented_lax_2ch/2/instance_0', 0,
+    ),
+)
+lax_2ch_diastole_slice_224_224_3d = TensorMap(
+    'lax_2ch_diastole_slice_224_224_3d',
+    Interpretation.CONTINUOUS,
+    shape=(224, 224, 1),
+    loss='logcosh',
+    normalization=ZeroMeanStd1(),
+    tensor_from_file=_slice_tensor(
+        'ukb_cardiac_mri/cine_segmented_lax_2ch/2/instance_0', 0,
+    ),
+)
 lax_3ch_diastole_slice0_3d = TensorMap(
     'lax_3ch_diastole_slice0_3d',
     Interpretation.CONTINUOUS,
@@ -425,6 +480,16 @@ def _mri_slice_blackout_tensor_from_file(tm, hd5, dependents={}):
         'ukb_cardiac_mri/cine_segmented_lax_3ch/2/instance_0', 0,
     ),
 )
+lax_3ch_diastole_slice_224_160_3d = TensorMap(
+    'lax_3ch_diastole_slice_224_160_3d',
+    Interpretation.CONTINUOUS,
+    shape=(224, 160, 1),
+    loss='logcosh',
+    normalization=ZeroMeanStd1(),
+    tensor_from_file=_slice_tensor(
+        'ukb_cardiac_mri/cine_segmented_lax_3ch/2/instance_0', 0,
+    ),
+)
 cine_segmented_ao_dist_slice0_3d = TensorMap(
     'cine_segmented_ao_dist_slice0_3d',
     Interpretation.CONTINUOUS,

model_zoo/ECG2AF/README.md

@@ -2,7 +2,44 @@
 This directory contains models and code for predicting incident atrial fibrillation from 12 lead resting ECGs, as described in our 
 [Circulation paper](https://www.ahajournals.org/doi/full/10.1161/CIRCULATIONAHA.121.057480).
 
-To perform inference with this model run:
+The raw model files are stored using `git lfs` so you must have it installed and localize the full ~200MB files with:
+```bash
+git lfs pull --include model_zoo/ECG2AF/ecg_5000_survival_curve_af_quadruple_task_mgh_v2021_05_21.h5
+git lfs pull --include model_zoo/ECG2AF/strip_*
+```
+
+To load the 12 lead model in a jupyter notebook (running with the ml4h docker or python library installed) see the [example](./ecg2af_infer.ipynb) or run:
+
+```python
+import numpy as np
+from tensorflow.keras.models import load_model
+from ml4h.models.model_factory import get_custom_objects
+from ml4h.tensormap.ukb.survival import mgb_afib_wrt_instance2
+from ml4h.tensormap.ukb.demographics import age_2_wide, af_dummy, sex_dummy3
+
+output_tensormaps = {tm.output_name(): tm for tm in [mgb_afib_wrt_instance2, age_2_wide, af_dummy, sex_dummy3]}
+custom_dict = get_custom_objects(list(output_tensormaps.values()))
+model = load_model('./ecg_5000_survival_curve_af_quadruple_task_mgh_v2021_05_21.h5', custom_objects=custom_dict)
+ecg = np.random.random((1, 5000, 12))
+prediction = model(ecg)
+```
+If above does not work you may need to use an absolute path in `load_model`.
+
+The model has 4 output heads: the survival curve prediction for incident atrial fibrillation, the classification of atrial fibrillation at the time of ECG, sex, and age regression.  Those outputs can be accessed with:
+```python
+for name, pred in zip(model.output_names, prediction):
+    otm = output_tensormaps[name]
+    if otm.is_survival_curve():
+        intervals = otm.shape[-1] // 2
+        days_per_bin = 1 + otm.days_window // intervals
+        predicted_survivals = np.cumprod(pred[:, :intervals], axis=1)
+        print(f'AF Risk {otm} prediction is: {str(1 - predicted_survivals[0, -1])}')
+    else:
+        print(f'{otm} prediction is {pred}')
+```
+
+
+To perform command line inference with this model run:
 ```bash
   python /path/to/ml4h/ml4h/recipes.py \
     --mode infer \
@@ -20,18 +57,23 @@ The model weights for the main model which performs incident atrial fibrillation
 age regression, sex classification and prevalent (at the time of ECG) atrial fibrillation:
 [ecg_5000_survival_curve_af_quadruple_task_mgh_v2021_05_21.h5](./ecg_5000_survival_curve_af_quadruple_task_mgh_v2021_05_21.h5)
 
-We also include single lead models for lead strip I:[strip_I_survival_curve_af_v2021_06_15.h5](./strip_I_survival_curve_af_v2021_06_15.h5)
+We also include single lead models for lead/strip I: [strip_I_survival_curve_af_v2021_06_15.h5](./strip_I_survival_curve_af_v2021_06_15.h5)
 and II: [strip_II_survival_curve_af_v2021_06_15.h5](./strip_II_survival_curve_af_v2021_06_15.h5)
 
-### Study Design
-Flow chart of study design
-![Flow chart of study design](./study_design.jpg)
+### Study design
+<div style="padding: 10px; background-color: white; display: inline-block;">
+    <img src="./study_design.jpg" alt="Flow chart of study design" />
+</div>
+
 ### Performance
 Risk stratification model comparison
-![Risk stratification model comparison](./km.jpg)
+<div style="padding: 10px; background-color: white; display: inline-block;">
+    <img src="./km.jpg" alt="Risk stratification model comparison" />
+</div>
+
 ### Salience
 Salience and Median waveforms from predicted risk extremes.
 ![Salience and Median waveforms](./salience.jpg)
 ### Architecture
 1D Convolutional neural net architecture
-![Convolutional neural net architecture](./architecture.png)
+![Convolutional neural net architecture](./architecture.png)