CheXNet – a brief evaluation

CheXNet – a brief evaluation

Chest X-Ray deep dreamed - our AI & deep learning future
Chest Radiograph from ChestX-ray14 dataset processed with the deep dream algorithm trained on ImageNet

Andrew Ng released CheXNet yesterday on ArXiv (citation) and promoted it with a tweet which caused a bit of a stir on the internet and related radiology social media sites like Aunt Minnie.  Before Radiologists throw away their board certifications and look for jobs as Uber drivers, a few comments on what this does and does not do.

First off, from the Machine Learning perspective, methodologies check out.  It uses a 121 layer DenseNet, which is a powerful convolutional neural network.  While code has not yet been provided, the DenseNet seems similar to code repositories online where 121 layers are a pre-made format.  80/20 split for Training/Validation seems pretty reasonable (from my friend, Kirk Borne), Random initialization, minibatches of 16 w/oversampling positive classes, and a progressively decaying validation loss are utilized.  Class activation mappings are used to visualize areas in the image most indicative of the activated class (in this case, pneumonia).  This is an interesting technique that can be used to provide some human-interpretable insights into the potentially opaque DenseNet.

The last Fully Connected (FC) layer is replaced by a single output (only one class is being tested for – pneumonia) coupled to a sigmoid function (an activation function – see here) to give a probability between 0 and 1.

The test portion of the study was 420 Chest X-rays read by four radiologists, one of whom was a thoracic specialist.  They could choose between the 14 pathologies in the ChestX-ray14 dataset, read blind without any clinical data.

So, a ROC curve was created, showing three radiologists similar to each other, and one outlier.The radiologists lie slightly under the ROC curve of the CheXNet classifier.  But, a miss is as good as a mile, so the claims of at or above radiologist performance are accurate, because math.  Addendum – Even though this claim would likely not meet statistical significance.  Thanks, Luke.

So that’s the study.  Now, I will pick some bones with the study.

First, only including one thoracic radiologist is relevant, if you are going to make ground truth agreement of 3 out of four radiologists.  General radiologists will be less specific than specialist radiologists, and that is one of the reasons why we have moved to specialty-specific reads over the last 20 years.  If the three general rads disagreed with the thoracic rad, the thoracic rad’s ground truth would be discarded.  Think about this – you would take the word of the generalist over the specialist, despite greater training.  Google didn’t do this in their retinal machine learning paper.  Instead, Google used their three retinal specialists as ground truth and then looked at how the non-specialty opthalmologists were able to evaluate that data and what it meant to the training dataset.  (Thanks, Melody!)

Second, the Wang ChestXray14 dataset is a dataset that I believe was data-mined from NIH radiology reports.  This means that for the dataset, ground truth was whatever the radiologists said it was.  I’m not casting aspersions on the NIH radiologists, as I am sure they are pretty good.  I’m simply saying that the dataset’s ground truth is what it says it is, not necessarily what the patient’s clinical condition was.  As proof of that, here are a few cells from the findings field on this dataset.  I’m not sure if the multiple classes strengthens or weakens Mr. Ng’s argument.

Findings field from the ChestX-ray14 dataset (representative)

In any case, the NIH radiologists more than a few times perhaps couldn’t tell either, or identified one finding as the cause of the other (Infiltrate & Pneumonia mentioned side by side) and at the top you have an “atelectasis vs consolidation vs Pneumonia” (as radiologists we say these things).  Perhaps I am missing something here and the classifier is making a stronger decision between the pathologies.  But without the sigmoid activation percentages for each class in the 14, I can’t tell.  Andrew, if you read this, I have the utmost respect for you and your team, and I have learned from you.  But I would love to know your rebuttal, and I would urge you to publish those results.  Or perhaps someone should do it for reproducibility.

Addendum:   Other radiologists with machine learning chops whom I respect are also concerned about how the ground truth was decided in the ChestXRay14 dataset.  This needs to be further looked into.

Finally, I’m bringing up these points not to be a killjoy, but to be balanced.  I think it is important to see this and prevent an administrator from making a really boneheaded decision of firing their radiologists to put in a computer diagnostic system (not in the US, but elsewhere) and realizing it doesn’t work after spending a vast sum of money on it.  Startups competing in the field who do not have healthcare experience need to be aware of these pitfalls in their product.  I’m saying this because real people could be really hurt and impacted if we don’t manage this transition into AI well.

Thanks for reading, and feel free to comment here or on twitter or linkedin to me: @drsxr

Machine Intelligence in Medical Imaging Conference – Report

blueI heard about the Society of Imaging Informatics in Medicine’s (SIIM) Scientific Conference on Machine Intelligence in Medical Imaging (C-MIMI) on Twitter.  Priced attractively, easy to get to, I’m interested in Machine Learning and it was the first radiology conference I’ve seen on this subject, so I went.  Organized on short notice so I was expecting a smaller conference.

cmimipacked

I almost didn’t get a seat.  It was packed.

The conference had real nuts and bolts presentations & discussions on healthcare imaging machine learning (ML).  Typically, these were Convolutional Neural Networks (CNN‘s/Convnets) but a few Random Forests (RF) and Support Vector Machines (SVM) sneaked in, particularly in hybrid models along with a CNN (c.f.  Microsoft).  Following comments assume some facility in understanding/working with Convnets.

Some consistent threads throughout the conference:

  • Most CNN’s were trained on Imagenet with the final fully connected (FC) layer removed; then re-trained on radiology data with a new classifer FC layer placed at the end.
  • Most CNN’s were using Imagenet standard three layer RGB input despite being greyscale.  This is of uncertain significance and importance.
  • The limiting of input matrices to grids less than image size is inherited from the Imagenet competitions (and legacy computational power).  Decreased resolution is a limiting factor in medical imaging applications, potentially worked-around by multi-scale CNN’s.
  • There is no central data repository for a good “Ground Truth” to develop improved machine imaging models.
  • Data augmentation methods are commonly used due to lower numbers of obtained cases.

Keith Dryer DO PhD gave an excellent lecture about the trajectory of machine imaging and how it will be an incremental process with AI growth more narrow in scope than projected, chiefly limited by applications.  At this time, CNN creation and investigation is principally an artisanal product with limited scalability.  There was a theme – “What is ground truth?” which in different instances is different things (path proven, followed through time, pathognomonic imaging appearance).

There was an excellent educational session from the FDA’s Berkman Sahiner.  The difference between certifying a type II or type III device may keep radiologists working longer than expected!  A type II device, like CAD, identifies a potential abnormality but does not make a treatment recommendation and therefore only requires a 510(k) application.  A type III device, as in an automated interpretation program creating diagnosis and treatment recommendations will require a more extensive application including clinical trials, and a new validation for any material changes.  One important insight (there were many) was that the FDA requires training and test data to be kept separate.   I believe this means that simple cross-validation is not acceptable nor sufficient for FDA approval or certification.  Adaptive systems may be a particularly challenging area for regulation, as similar to the ONC, significant changes to the software of the algorithm will require a new certification/approval process.

Industry papers were presented from HK Lau of Arterys, Xiang Zhou of Siemens, Xia Li of GE, and Eldad Elnekave of Zebra medical.  The Zebra medical presentation was impressive, citing their use of the Google Inception V3 model and a false-color contrast limited adaptive histogram equalization algorithm, which not only provides high image contrast with low noise, but also gets around the 3-channel RGB issue.  Given statistics for their CAD program were impressive at 94% accuracy compared to a radiologist at 89% accuracy.

Scientific Papers were presented by Matthew Chen, Stanford; Synho Do, Harvard; Curtis Langlotz, Stanford; David Golan, Stanford; Paras Lakhani, Thomas Jefferson; Panagiotis Korfiatis, Mayo Clinic; Zeynettin Akkus, Mayo Clinic; Etka Bullar, U Saskatchewan; Mahmudur Rahman, Morgan State U; Kent Ogden SUNY upstate.

Ronald Summers, MD PhD from the NIH gave a presentation on the work from his lab in conjunction with Holger Roth, detailing the specific CNN approaches to Lymph Node detection, Anatomic level detection, Vertebral body segmentation, Pancreas Segmentation, and colon polyp screening with CT-colonography, which had high False Positives.  In his experience, deeper models performed better.  His lab also changes unstructured radiology reporting into structured reporting through ML techniques.

Abdul Halabi of NVIDIA gave an impressive presentation on the supercomputer-like DGX-1 GPU cluster (5 deliveries to date, the fifth of which was to Mass. General, a steal at over $100K), and the new Pascal architecture in the P4 & P40 GPU’s.  60X performance on AlexNet vs the original version/GPU configuration in 2012.  Very impressive.

Sayan Pathak of Microsoft Research and the Inner Eye team gave a good presentation where he demonstrated that a RF was really just a 2 layer DNN, i.e. a sparse 2 layer perceptron.   Combining this with a CNN (dNDE.NET), it beat googLENet’s latest version in the Imagenet arms race.  However, as one needs to solve for both structures simultaneously, it is an expensive (long, intense) computation.

Closing points were the following:

  • Most devs currently using Python – Tensorflow +/- Keras with fewer using CAFFE off of  Modelzoo
  • DICOM -> NIFTI -> DICOM
  • De-identification of data is a problem, even moreso when considering longitudinal followup.
  • Matching accuracy to the radiologist’s report may not be as important as actual outcomes report.
  • There was a lot of interest in organizing a competition to advance medical imaging, c.f. Kaggle.
  • Radiologists aren’t obsolete just yet.

It was a great conference.  An unexpected delight.  Food for your head!

 

 

 

Value and Risk: the Radiologist’s perspective (Value as risk series #4)

Public DomainMuch can be written about Value-based care. I’ll focus on imaging risk management from a radiologist’s perspective. What it looks like from the Hospital’s perspective , the Insurer’s perspective, and in general have been discussed previously.

When technology was in shorter supply, radiologists were gatekeepers of limited Ultrasound, CT and MRI resources. Need-based radiologist approval was necessary for ‘advanced imaging’. The exams were expensive and needed to be protocoled correctly to maximize utility. This encouraged clinician-radiologist interaction – thus our reputation as “The Doctor’s doctor.”

In the 1990’s-2000’s , there was an explosion in imaging utilization and installed equipment. Imaging was used to maximize throughput, minimize patient wait times and decrease length of hospital stays. A more laissez-faire attitude prevailed where gatekeeping was frowned upon.

With a transition to value-based care, the gatekeeping role of radiology will return. Instead of assigning access to imaging resources on basis of limited availability, we need to consider ROI (return on investment) in the context of whether the imaging study will be likely to improve outcome vs. cost. (1) Clinical Decision Support (CDS) tools can help automating imaging appropriateness and value. (2)

The bundle’s economics are capitation of a single care episode for a designated ICD-10 encounter. This extends across the inpatient stay and related readmissions up to 30 days after discharge (CMS BPCI Model 4). A review of current Model 4 conditions show mostly joint replacements, spinal fusion, & our example case of CABG (Coronary Artery Bypass Graft).

Post CABG, a daily Chest X-ray (CXR) protocol may be ordered – very reasonable for an intubated & sedated patient. However, an improving non-intubated awake patient may not need a daily CXR. Six Sigma analysis would empirically classify this as waste – and a data analysis of outcomes may confirm it.

Imaging-wise, patients need a CXR preoperatively, & periodically thereafter. A certain percentage of patients will develop complications that require at least one CT scan of the chest. Readmissions will also require re-imaging, usually CT. There will also be additional imaging due to complications or even incidental findings if not contractually excluded (CT/CTA/MRI Brain, CT/CTA neck, CT/CTA/US/MRI abdomen, Thoracic/Lumbar Spine CT/MRI, fluoroscopy for diaphragmatic paralysis or feeding tube placement, etc…). All these need to be accounted for.

www.n2value.com

 

In the fee-for-service world, the ordered study is performed and billed.  In bundled care, payments for the episode of care are distributed to stakeholders according to a pre-defined allocation.

Practically, one needs to retrospectively evaluate over a multi-year period how many and what type of imaging studies were performed in patients with the bundled procedure code. (3) It is helpful to get sufficient statistical power for the analysis and note trends in both number of studies and reimbursement. Breaking down the total spend into professional and technical components is also useful to understand all stakeholder’s viewpoints. Evaluate both the number of studies performed and the charges, which translates into dollars by multiplying by your practice’s reimbursement percentage. Forward-thinking members of the Radiology community at Nieman HPI  are providing DRG-related tools such as ICE-T to help estimate these costs (used in above image). Ultimately one ends up with a formula similar to this:

CABG imaging spend = CXR’s+CT Chest+ CTA chest+ other imaging studies.

Where money will be lost is at the margins – patients who need multiple imaging studies, either due to complications or incidental findings. With between a 2% to 3% death rate for CABG and recognizing 30% of all Medicare expenditures are caused by the 5% of beneficiaries that die, with 1/3 of that cost in the last month of life (Barnato et al), this must be accounted for. An overly simplistic evaluation of the imaging needs of CABG will result in underallocation of funds for the radiologist, resulting in per-study payment dropping  – the old trap of running faster to stay in place.

Payment to the radiologist could either be one of two models:

First, fixed payment per RVU. Advantageous to the radiologist, it insulates from risk-sharing. Ordered studies are read for a negotiated rate. The hospital bears the cost of excess imaging. For a radiologist in an independent private practice providing services through an exclusive contract, allowing the hospital to assume the risk on the bundle may be best.

Second, a fixed (capitated) payment per bundled patient for imaging services may be made to the radiologist. This can either be in the form of a fixed dollar amount or a fixed percentage of the bundle.  (Frameworks for Radiology Practice Participation, Nieman HPI)  This puts the radiologist at-risk, in a potentially harmful way. The disconnect is that the supervising physicians (cardio-thoracic surgeon, intensivist, hospitalist) will be focusing on improving outcome, decreasing length of stay, or reducing readmission rates, not imaging volume. Ordering imaging studies (particularly advanced imaging) may help with diagnostic certitude and fulfill their goals. This has the unpleasant consequence of the radiologist’s per study income decreasing when they have no control over the ordering of the studies and, in fact, it may benefit other parties to overuse imaging to meet other quality metrics. The radiology practice manager should proceed with caution if his radiologists are in an employed model but the CT surgeon & intensivists are not. Building in periodic reviews of expected vs. actual imaging use with potential re-allocations of the bundle’s payment might help to curb over-ordering. Interestingly, in this model the radiologist profits by doing less!

Where the radiologist can add value is in analysis, deferring imaging unlikely to impact care. Reviewing data and creating predictive analytics designed to predict outcomes adds value while, if correctly designed, avoiding more than the standard baseline of risk. (see John’s Hopkins Sepsis prediction model). In patients unlikely to have poor outcomes, additional imaging requests can be gently denied and clinicians reassured. I.e. “This patient has a 98% chance of being discharged without readmission. Why a lumbar spine MRI?” (c.f. AK Moriarty et al) Or, “In this model patients with these parameters only need a CXR every third day. Let’s implement this protocol.” The radiologist returns to a gatekeeping role, creating value by managing risk, intelligently.

Let’s return to our risk/reward matrix:

www.n2value.com

 

For the radiologist in the bundled example receiving fixed payments:

 

Low Risk/Low Reward: Daily CXR’s for the bundled patients.

 

High Risk/Low Reward: Excess advanced imaging (more work for no change in pay)

 

High Risk/High Reward: Arbitrarily denying advanced imaging without a data-driven model (bad outcomes = loss of job, lawsuit risk)

 

Low Risk/High Reward: Analysis & Predictive modeling to protocol what studies can be omitted in which patients without compromising care.

 

I, and others, believe that bundled payments have been put in place not only to decrease healthcare costs, but to facilitate transitioning from the old FFS system to the value-based ‘at risk’ payment system, and ultimately capitated care. (Rand Corp, Technical Report TR-562/20) By developing analytics capabilities, radiology providers will be able to adapt to these new ‘at-risk’ payment models and drive adjustments to care delivery to improve or maintain the community standard of care at the same or lower cost.

  1. B Ingraham, K Miller et al. Am Coll Radiol 2016 in press
  2. AK Moriarty, C Klochko et al J Am Coll Radiol 2015;12:358-363
  3. D Seidenwurm FJ Lexa J Am Coll Radiol 2016 in press

Defining value in healthcare through risk

High-low-norisk

For a new definition of value, it’s helpful to go back to the conceptual basis of payment for medical professional services under the RBRVS. Payment for physician services is divided into three components: Physician work, practice expense, and a risk component.

Replace physician with provider, and then extrapolate to larger entities.

Currently, payer (insurer, CMS, etc…) and best practice (specialty societies, associations like HFMA, ancillary staff associations) guidelines exist. This has reduced some variation among providers, and there is an active interest to continue this direction. For example, level 1 E&M clearly differs from a level 5 E&M – one might disagree whether a visit is a level 3 or 4, but you shouldn’t see the level 1 upcoded to 5. Physician work is generally quantifiable in either patients seen or procedures done, and for any corporate/employed practice, most physicians will be working towards the level of productivity they have contractually agreed to, or they will be let go/contracts renegotiated. Let’s hope they are fairly compensated for their efforts and not subjected solely to RVU production targets, which are falling out of favor vs. more sophisticated models (c.f. Craig Pedersen, Insight Health Partners).

Unless there is mismanagement in this category, provider work is usually controllable, measurable, and with some variation due to provider skill, age, and practice goals, consistent. For those physicians who have been vertically integrated, their current EHR burdens and compliance directives may place a cap on productivity.

Practice expenses represent those fixed expenses and variable expenses in healthcare – rent, taxes, facility maintenance, and consumables (medical supplies, pharmaceuticals, and medical devices). Most are fairly straightforward from an accounting standpoint. Medical supplies, pharmaceuticals, and devices are expenses that need management, with room for opportunity. ACO and super ACO/CIO organizations and purchasing consortiums such as Novation, Amerinet, and and Premier have been formed to help manage these costs.

Practice expense costs are identifiable, and once identified, controllable. Initially, six sigma management tools work well here. For all but the most peripheral, this has happened/is happening, and there are no magic bullets out there beyond continued monitoring of systems & processes as they evolve over time as drift and ripple effects may impact previously optimized areas.

This leaves the last variable – risk. Risk was thought of as a proxy for malpractice/legal costs. However, in the new world of variable payments, there is not only downside risk in this category, but the pleasant possibility of upside risk.

It reasons that if your provider costs are reasonably fixed, and practice expenses are as fixed as you can get them at the moment, that you should look to the risk category as an opportunity for profit.

As a Wall St. options trader, the only variable that really mattered to me for the price of the derivative product was the volatility of the option – the measure of its inherent risk. We profited by selling options (effectively, insurance) when that implied volatility was higher than the actual market volatility, or buying them when it was too low. Why can’t we do the same in healthcare?

What is value in this context? The profit or loss arising from the assumption and management of risk. Therefore, the management of risk in a value-based care setting allows for the possibility of a disproportionate financial return.

www.n2value.com

The sweet spot is Low Risk/High Return. This is where discovering a fundamental mispricing can return disproportionately vs. exposure to risk.

Apply this risk matrix to:

  • 1 – A medium sized insurer, struggling with hospital mergers and former large employers bypassing the insurer directly and contracting with the hospitals.
  • 2 – A larger integrated hospital system with at-risk payments/ACO model, employed physicians, and local competitors which is struggling to provide good care in the low margin environment.
  • 3 – group radiology practice which contracts with a hospital system and a few outpatient providers.

& things get interesting. On to the next post!

Some reflections on the ongoing shift from volume to value

As an intuitive and inductive thinker, I often use facts to prove or disprove my biases. This may make me a poor researcher, though I believe I would have been popular in circa 1200 academic circles. Serendipity plays a role; yes I’m a big Nassim Taleb fan – sometimes in the seeking, unexpected answers appear. Luckily, I’m correct more often than not. But honestly – in predicting widely you miss more widely.

One of my early mentors from Wall St. addressed this with me in the infancy of my career – take Babe Ruth’s batting average of .342 . This meant that two out of three times at bat, Babe Ruth struck out. However, he was trying to hit home runs. There is a big difference between being a base hit player and a home run hitter. What stakes are you playing for?

With that said, this Blog is for exploring topics I find of interest pertaining mostly to healthcare and technology. The blog has been less active lately, not only due to my own busy personal life (!) but also because I have sought more up-to-date information about advancing trends in both the healthcare payment sector and the IT/Tech sector as it applies to medicine. I’m also diving deeper into Radiology and Imaging. As I’ve gone through my data science growth phase, I’ll probably blog less on that topic except as it pertains to machine learning.

The evolution of the volume to value transition is ongoing as many providers are beginning to be subject to at least a degree of ‘at-risk’ payment. Stages of ‘at-risk’ payment have been well characterized – this slide by Jacque Sokolov MD at SSB solutions is representative:

Sokolove - SSB solutions slide 1

In 2015, approximately 20% of medicare spend was value-based, with CMS’s goal 50% by 2020. Currently providers are ‘testing the waters’ with <20% of providers accepting over 40% risk-based payments (c.f. Kimberly White MBA, Numerof & Associates). Obviously the more successful of these will be larger, more data-rich and data-utilizing providers.

However, all is not well in the value-based-payment world. In fact, this year United Health Care announced it is pulling its insurance products out of most of the ACA exchange marketplaces. While UHC products were a small share of the exchanges, it sends a powerful message when a major insurer declines to participate. Recall most ACO’s (~75%) did not produce cost savings in 2014, although more recent data was more encouraging (c.f. Sokolov).   Notably, out of the 32 Pioneer ACO’s that started, only 9 are left (30%) (ref. CMS). The road to value is not a certain path at all.

So, with these things in mind, how do we negotiate the waters? Specifically, as radiologists, how do we manage the shift from volume to value, and what does it mean for us? How is value defined for Radiology? What is it not? Value is NOT what most people think it is. I define value as: the cost savings arising from the assumption and management of risk. We’ll explore this in my next post.